Sensors

33 pages, 709 KiB

Open AccessArticle

An Infrastructure for Enabling Dynamic Fault Tolerance in Highly-Reliable Adaptive Distributed Embedded Systems Based on Switched Ethernet

by Alberto Ballesteros, Manuel Barranco, Julián Proenza, Luís Almeida, Francisco Pozo and Pere Palmer-Rodríguez

Sensors 2022, 22(18), 7099; https://doi.org/10.3390/s22187099 - 19 Sep 2022

Cited by 8 | Viewed by 2580

Abstract

Distributed Embedded Systems (DESs) carrying out critical tasks must be highly reliable and hard in real-time. Moreover, to operate in dynamic operational contexts in an effective and efficient manner, they must also be adaptive. Adaptivity is particularly interesting from a dependability perspective, as [...] Read more.

Distributed Embedded Systems (DESs) carrying out critical tasks must be highly reliable and hard in real-time. Moreover, to operate in dynamic operational contexts in an effective and efficient manner, they must also be adaptive. Adaptivity is particularly interesting from a dependability perspective, as it can be used to develop dynamic fault tolerance mechanisms, which, in combination with static ones, make it possible to provide better and more efficient fault tolerance. However, constructing a DES with such complexity presents many challenges. This is because all the mechanisms that support fault tolerance, real-time, and adaptivity must be designed to operate in a coordinated manner. This paper presents the Dynamic Fault Tolerance for Flexible Time-Triggered Ethernet (DFT4FTT), a self-reconfigurable infrastructure for implementing highly reliable adaptive DES. Here, we describe the design of its hardware and software architecture and the main set of mechanisms, with a focus on fault tolerance. Full article

(This article belongs to the Special Issue Feature Papers in the Sensor Networks Section 2022)

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23 pages, 7981 KiB

Open AccessArticle

Sub-Surface Defect Depth Approximation in Cold Infrared Thermography

by Siavash Doshvarpassand and Xiangyu Wang

Sensors 2022, 22(18), 7098; https://doi.org/10.3390/s22187098 - 19 Sep 2022

Cited by 4 | Viewed by 2712

Abstract

Detection and characterisation of hidden corrosion are considered challenging yet crucial activities in many sensitive industrial plants where preventing the loss of containment or structural reliability are paramount. In the last two decades, infrared (IR) thermography has proved to be a reliable means [...] Read more.

Detection and characterisation of hidden corrosion are considered challenging yet crucial activities in many sensitive industrial plants where preventing the loss of containment or structural reliability are paramount. In the last two decades, infrared (IR) thermography has proved to be a reliable means for inspection of corrosion or other sub-surface anomalies in low to mid thickness metallic mediums. The foundation of using IR thermography for defect detection and characterisation is based on active thermography. In this method of inspection, an external excitation source is deployed for the purpose of stimulating thermal evolutions inside objects. The presence of sub-surface defects disrupts the evolution of electromagnetic pulse inside an object. The reflection of altered pulse at the surface can be recorded through thermal camera in the form of temperature anomalies. Through authors’ previous works, cold thermography has shown that it can be a viable defect detection alternative to the most commonly used means of active thermography, known as heating. In the current work, the characterisation of defect dimensions, i.e., depth and diameter, has been explored. A simple analytical model for thermal contrast over defect is used in order to approximate the defect depth and diameter. This is achieved by comparing the similarities of the model and the experimental contrast time-series. A method of time-series similarity measurement known as dynamic time wrapping (DTW) is used to score the similarity between a pair of model and experiment time-series. The final outcome of the proposed experimental setup has revealed that there is a good potential to predict the metal loss of up to 50% in mid-thickness substrate even by deploying a less accurate nonradiometric thermal device and no advanced image processing. Full article

(This article belongs to the Special Issue Advanced Sensing Technologies in Structural Health Monitoring and Its Applications)

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22 pages, 2953 KiB

Open AccessArticle

Performance Analysis of NB-IoT Uplink in Low Earth Orbit Non-Terrestrial Networks

by Min-Gyu Kim and Han-Shin Jo

Sensors 2022, 22(18), 7097; https://doi.org/10.3390/s22187097 - 19 Sep 2022

Cited by 7 | Viewed by 5115

Abstract

The 3rd Generation Partnership Project (3GPP) narrowband Internet of Things (NB-IoT) over non-terrestrial networks (NTN) is the most promising candidate technology supporting 5G massive machine-type communication. Compared to geostationary earth orbit, low earth orbit (LEO) satellite communication has the advantage of low propagation [...] Read more.

The 3rd Generation Partnership Project (3GPP) narrowband Internet of Things (NB-IoT) over non-terrestrial networks (NTN) is the most promising candidate technology supporting 5G massive machine-type communication. Compared to geostationary earth orbit, low earth orbit (LEO) satellite communication has the advantage of low propagation loss, but suffers from high Doppler shift. The 3GPP proposes Doppler shift pre-compensation for each beam region of the satellite. However, user equipment farther from the beam center has significant residual Doppler shifts even after pre-compensation, which degrades link performance. This study proposes residual Doppler shift compensation by adding demodulation reference signal symbols and reducing satellite beam coverage. The block error rate (BLER) data are obtained using link-level simulation with the proposed technique. Since the communication time provided by a single LEO satellite moving fast is short, many LEO satellites are necessary for seamless 24-h communication. Therefore, with the BLER data, we analyze the link budget for actual three-dimensional orbits with a maximum of 162 LEO satellites. We finally investigate the effect of the proposed technique on performance metrics such as the per-day total service time and maximum persistent service time, considering the number of satellites and the satellite spacing. The results show that a more prolonged and continuous communication service is possible with significantly fewer satellites using the proposed technique. Full article

(This article belongs to the Special Issue Massive Machine-Type Communications towards 6G)

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15 pages, 217 KiB

Open AccessArticle

Mobile Forensics: Repeatable and Non-Repeatable Technical Assessments

by Raffaele Cuomo, Davide D’Agostino and Mario Ianulardo

Sensors 2022, 22(18), 7096; https://doi.org/10.3390/s22187096 - 19 Sep 2022

Cited by 1 | Viewed by 3804

Abstract

This paper presents several scenarios where digital evidence can be collected from mobile devices, their legal value keeping untouched. The paper describes a robust methodology for mobile forensics developed through on-field experiences directly gained by the authors over the last 10 years and [...] Read more.

This paper presents several scenarios where digital evidence can be collected from mobile devices, their legal value keeping untouched. The paper describes a robust methodology for mobile forensics developed through on-field experiences directly gained by the authors over the last 10 years and many real court cases. The results show that mobile forensics, digital analysis of smartphone Android or iOS can be obtained in two ways: on the one hand, data extraction must follow the best practice of the repeatability procedure; on the other hand, the extraction of the data must follow the best practice of the non-repeatability procedure. The laboratory study of the two methods for extracting digital data from mobile phones, for use as evidence in court trials, has shown that the same evidence can be obtained even when the procedure of unavailability of file mining activities has been adopted. Indeed, thanks to laboratory tests, the existence of multiple files frequently and continuously subjected to changes generated by the presence of several hashes found at forensic extractions conducted in very short moments of time (sometimes not exceeding 15 min) has been proven. If, on the other hand, the examination of a device is entrusted to a judicial police officer in order to conduct a forensic analysis to acquire data produced and managed by the user (such as images, audio, video, documents, SMS, MMS, chat conversations, address book content, etc.) we have sufficient grounds to believe that such examination can be organized according to the system of repeatable technical assessments. Full article

(This article belongs to the Special Issue Feature Extraction and Forensic Image Analysis)

15 pages, 1578 KiB

Open AccessArticle

Smartphone-Based Ecological Momentary Assessment for Collecting Pain and Function Data for Those with Low Back Pain

by Ekjyot Kaur, Pari Delir Haghighi, Flavia M. Cicuttini and Donna M. Urquhart

Sensors 2022, 22(18), 7095; https://doi.org/10.3390/s22187095 - 19 Sep 2022

Cited by 2 | Viewed by 2639

Abstract

Smartphone-based ecological momentary assessment (EMA) methods are widely used for data collection and monitoring in healthcare but their uptake clinically has been limited. Low back pain, a condition with limited effective treatments, has the potential to benefit from EMA. This study aimed to [...] Read more.

Smartphone-based ecological momentary assessment (EMA) methods are widely used for data collection and monitoring in healthcare but their uptake clinically has been limited. Low back pain, a condition with limited effective treatments, has the potential to benefit from EMA. This study aimed to (i) determine the feasibility of collecting pain and function data using smartphone-based EMA, (ii) examine pain data collected using EMA compared to traditional methods, (iii) characterize individuals’ progress in relation to pain and function, and (iv) investigate the appropriation of the method. Our results showed that an individual’s ‘pain intensity index’ provided a measure of the burden of their low back pain, which differed from but complemented traditional ‘change in pain intensity’ measures. We found significant variations in the pain and function over the course of an individual’s back pain that was not captured by the cohort’s mean scores, the approach currently used as the gold standard in clinical trials. The EMA method was highly acceptable to the participants, and the Model of Technology Appropriation provided information on technology adoption. This study highlights the potential of the smartphone-based EMA method for enhancing the collection of outcome data and providing a personalized approach to the management of low back pain. Full article

(This article belongs to the Special Issue Advances in E-health and Mobile Health Monitoring)

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18 pages, 9295 KiB

Open AccessArticle

Gait Detection from a Wrist-Worn Sensor Using Machine Learning Methods: A Daily Living Study in Older Adults and People with Parkinson’s Disease

by Yonatan E. Brand, Dafna Schwartz, Eran Gazit, Aron S. Buchman, Ran Gilad-Bachrach and Jeffrey M. Hausdorff

Sensors 2022, 22(18), 7094; https://doi.org/10.3390/s22187094 - 19 Sep 2022

Cited by 13 | Viewed by 4421

Abstract

Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly [...] Read more.

Remote assessment of the gait of older adults (OAs) during daily living using wrist-worn sensors has the potential to augment clinical care and mobility research. However, hand movements can degrade gait detection from wrist-sensor recordings. To address this challenge, we developed an anomaly detection algorithm and compared its performance to four previously published gait detection algorithms. Multiday accelerometer recordings from a wrist-worn and lower-back sensor (i.e., the “gold-standard” reference) were obtained in 30 OAs, 60% with Parkinson’s disease (PD). The area under the receiver operator curve (AUC) and the area under the precision–recall curve (AUPRC) were used to evaluate the performance of the algorithms. The anomaly detection algorithm obtained AUCs of 0.80 and 0.74 for OAs and PD, respectively, but AUPRCs of 0.23 and 0.31 for OAs and PD, respectively. The best performing detection algorithm, a deep convolutional neural network (DCNN), exhibited high AUCs (i.e., 0.94 for OAs and 0.89 for PD) but lower AUPRCs (i.e., 0.66 for OAs and 0.60 for PD), indicating trade-offs between precision and recall. When choosing a classification threshold of 0.9 (i.e., opting for high precision) for the DCNN algorithm, strong correlations (r > 0.8) were observed between daily living walking time estimates based on the lower-back (reference) sensor and the wrist sensor. Further, gait quality measures were significantly different in OAs and PD compared to healthy adults. These results demonstrate that daily living gait can be quantified using a wrist-worn sensor. Full article

(This article belongs to the Special Issue Inertial Sensors for Clinically Relevant Mobility Outcome Measures)

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20 pages, 8851 KiB

Open AccessArticle

Blind Fault Extraction of Rolling-Bearing Compound Fault Based on Improved Morphological Filtering and Sparse Component Analysis

by Wensong Xie, Jun Zhou and Tao Liu

Sensors 2022, 22(18), 7093; https://doi.org/10.3390/s22187093 - 19 Sep 2022

Cited by 5 | Viewed by 1930

Abstract

In order to effectively separate and extract bearing composite faults, in view of the non-linearity, strong interference and unknown number of fault source signals of the measured fault signals, a composite fault-diagnosis blind extraction method based on improved morphological filtering of

\sin C

[...] Read more.

In order to effectively separate and extract bearing composite faults, in view of the non-linearity, strong interference and unknown number of fault source signals of the measured fault signals, a composite fault-diagnosis blind extraction method based on improved morphological filtering of

\sin C

function (SMF), density peak clustering (DPC) and orthogonal matching pursuit (OMP) is proposed. In this method, the

\sin C

function is used as the structural element of the morphological filter for the first time to improve the traditional morphological filter. After the observation signal is processed by the improved morphological filter, the impact characteristics of the signal are improved, and the signal meets the sparsity. Then, on the premise that the number of clustering is unknown, the density peak algorithm is used to cluster sparse signals to obtain the clustering center, which is equivalent to the hybrid matrix. Finally, the hybrid matrix is transformed into a sensing matrix, and the signal is transformed into the frequency domain to complete the compressive sensing and reconstruction of the signal in the frequency domain. Both simulation and measured signal results show that this algorithm can effectively complete the blind separation of rolling bearing faults when the number of fault sources is unknown, and the time cost can be reduced by about 75%. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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18 pages, 5580 KiB

Open AccessArticle

A New Construction of

4^{q}

-QAM Golay Complementary Sequences

by Gang Peng, Zhiren Han and Dewen Li

Sensors 2022, 22(18), 7092; https://doi.org/10.3390/s22187092 - 19 Sep 2022

Cited by 1 | Viewed by 1887

Abstract

Quadrature amplitude modulation (QAM) constellation and Golay complementary sequences (GCSs) are usually applied in orthogonal frequency division multiplexing (OFDM) systems to obtain a higher data rate and a lower peak-to-mean envelope power ratio (PMEPR). In this paper, after a sufficient search of the [...] Read more.

Quadrature amplitude modulation (QAM) constellation and Golay complementary sequences (GCSs) are usually applied in orthogonal frequency division multiplexing (OFDM) systems to obtain a higher data rate and a lower peak-to-mean envelope power ratio (PMEPR). In this paper, after a sufficient search of the literature, it was found that increasing the family size is an effective way to improve the data rate, and the family size is mainly determined by the number of offsets in the general structure of QAM GCSs. Under the guidance of this idea, we propose a new construction for

4^{q}

-QAM GCSs through generalized Boolean functions (GBFs) based on a new description of a

4^{q}

-QAM constellation, which aims to enlarge the family size of GCSs and obtain a low PMEPR. Furthermore, a previous construction of

4^{q}

-QAM GCSs presented by Li has been proved to be a special case of the new one, and the family size of new sequences is much larger than those previously mentioned, which means that there was a great improvement in the data rate. On the other hand, a previous construction of 16-QAM GCSs presented by Zeng is also a special case of the new one in this paper, when

q = 2

. In the meantime, the proposed sequences have the same PMEPR upper bound as the previously mentioned sequences presented by Li when applied in OFDM systems, which increase the data rate without degrading the PMEPR performance. The theoretical analysis and simulation results show that the proposed new sequences can achieve a higher data rate and a low PMEPR. Full article

(This article belongs to the Special Issue Technologies of Highly Efficient Telecommunication Systems and Devices)

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14 pages, 4170 KiB

Open AccessArticle

Correlation Study between the Organic Compounds and Ripening Stages of Oil Palm Fruitlets Based on the Raman Spectra

by Muhammad Haziq Imran Md Azmi, Fazida Hanim Hashim, Aqilah Baseri Huddin and Mohd Shaiful Sajab

Sensors 2022, 22(18), 7091; https://doi.org/10.3390/s22187091 - 19 Sep 2022

Cited by 6 | Viewed by 2404

Abstract

The degree of maturity of oil palm fresh fruit bunches (FFB) at the time of harvest heavily affects oil production, which is expressed in the oil extraction rate (OER). Oil palm harvests must be harvested at their optimum maturity to maximize oil yield [...] Read more.

The degree of maturity of oil palm fresh fruit bunches (FFB) at the time of harvest heavily affects oil production, which is expressed in the oil extraction rate (OER). Oil palm harvests must be harvested at their optimum maturity to maximize oil yield if a rapid, non-intrusive, and accurate method is available to determine their level of maturity. This study demonstrates the potential of implementing Raman spectroscopy for determining the maturity of oil palm fruitlets. A ripeness classification algorithm has been developed utilizing machine learning by classifying the components of organic compounds such as β-carotene, amino acid, etc. as parameters to distinguish the ripeness of fruits. In this study, 47 oil palm fruitlets spectra from three different ripeness levels—under ripe, ripe, and over ripe—were examined. To classify the oil palm fruitlets into three maturity categories, the extracted features were put to the test using 31 machine learning models. It was discovered that the Medium, Weighted KNN, and Trilayered Neural Network classifier has a maximum overall accuracy of 90.9% by using four significant features extracted from the peaks as the predictors. To conclude, the Raman spectroscopy method may offer a precise and efficient means to evaluate the maturity level of oil palm fruitlets. Full article

(This article belongs to the Special Issue AI-Based Sensors and Sensing Systems for Smart Agriculture)

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15 pages, 4860 KiB

Open AccessArticle

Automatic Meter Reading from UAV Inspection Photos in the Substation by Combining YOLOv5s and DeeplabV3+

by Guanghong Deng, Tongbin Huang, Baihao Lin, Hongkai Liu, Rui Yang and Wenlong Jing

Sensors 2022, 22(18), 7090; https://doi.org/10.3390/s22187090 - 19 Sep 2022

Cited by 14 | Viewed by 3439

Abstract

The combination of unmanned aerial vehicles (UAVs) and artificial intelligence is significant and is a key topic in recent substation inspection applications; and meter reading is one of the challenging tasks. This paper proposes a method based on the combination of YOLOv5s object [...] Read more.

The combination of unmanned aerial vehicles (UAVs) and artificial intelligence is significant and is a key topic in recent substation inspection applications; and meter reading is one of the challenging tasks. This paper proposes a method based on the combination of YOLOv5s object detection and Deeplabv3+ image segmentation to obtain meter readings through the post-processing of segmented images. Firstly, YOLOv5s was introduced to detect the meter dial area and the meter was classified. Following this, the detected and classified images were passed to the image segmentation algorithm. The backbone network of the Deeplabv3+ algorithm was improved by using the MobileNetv2 network, and the model size was reduced on the premise that the effective extraction of tick marks and pointers was ensured. To account for the inaccurate reading of the meter, the divided pointer and scale area were corroded first, and then the concentric circle sampling method was used to flatten the circular dial area into a rectangular area. Several analog meter readings were calculated by flattening the area scale distance. The experimental results show that the mean average precision of 50 (mAP50) of the YOLOv5s model with this method in this data set reached 99.58%, that the single detection speed reached 22.2 ms, and that the mean intersection over union (mIoU) of the image segmentation model reached 78.92%, 76.15%, 79.12%, 81.17%, and 75.73%, respectively. The single segmentation speed reached 35.1 ms. At the same time, the effects of various commonly used detection and segmentation algorithms on the recognition of meter readings were compared. The results show that the method in this paper significantly improved the accuracy and practicability of substation meter reading detection in complex situations. Full article

(This article belongs to the Special Issue Artificial Intelligence in Computer Vision: Methods and Applications)

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22 pages, 6374 KiB

Open AccessArticle

Improving the Accuracy of an R-CNN-Based Crack Identification System Using Different Preprocessing Algorithms

by Mian Zhao, Peixin Shi, Xunqian Xu, Xiangyang Xu, Wei Liu and Hao Yang

Sensors 2022, 22(18), 7089; https://doi.org/10.3390/s22187089 - 19 Sep 2022

Cited by 15 | Viewed by 3086

Abstract

The accurate intelligent identification and detection of road cracks is a key issue in road maintenance, and it has become popular to perform this task through the field of computer vision. In this paper, we proposed a deep learning-based crack detection method that [...] Read more.

The accurate intelligent identification and detection of road cracks is a key issue in road maintenance, and it has become popular to perform this task through the field of computer vision. In this paper, we proposed a deep learning-based crack detection method that initially uses the idea of image sparse representation and compressed sensing to preprocess the datasets. Only the pixels that represent the crack features remain, while most pixels of non-crack features are relatively sparse, which can significantly improve the accuracy and efficiency of crack identification. The proposed method achieved good results based on the limited datasets of crack images. Various algorithms were tested, namely, linear smooth, median filtering, Gaussian smooth, and grayscale threshold, where the optimal parameters of the various algorithms were analyzed and trained with faster regions with convolutional neural network features (faster R-CNN). The results of the experiments showed that the proposed method has good robustness, with higher detection efficiency in the presence of, for example, road markings, shallow cracks, multiple cracks, and blurring. The result shows that the improvement of mean average precision (mAP) can reach 5% compared with the original method. Full article

(This article belongs to the Special Issue Intelligent Sensing Technologies in Structural Health Monitoring)

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21 pages, 7335 KiB

Open AccessArticle

A Lightweight YOLOv5-MNE Algorithm for SAR Ship Detection

by Lei Pang, Baoxuan Li, Fengli Zhang, Xichen Meng and Lu Zhang

Sensors 2022, 22(18), 7088; https://doi.org/10.3390/s22187088 - 19 Sep 2022

Cited by 13 | Viewed by 3109

Abstract

Unlike optical satellites, synthetic aperture radar (SAR) satellites can operate all day and in all weather conditions, so they have a broad range of applications in the field of ocean monitoring. The ship targets’ contour information from SAR images is often unclear, and [...] Read more.

Unlike optical satellites, synthetic aperture radar (SAR) satellites can operate all day and in all weather conditions, so they have a broad range of applications in the field of ocean monitoring. The ship targets’ contour information from SAR images is often unclear, and the background is complicated due to the influence of sea clutter and proximity to land, leading to the accuracy problem of ship monitoring. Compared with traditional methods, deep learning has powerful data processing ability and feature extraction ability, but its complex model and calculations lead to a certain degree of difficulty. To solve this problem, we propose a lightweight YOLOV5-MNE, which significantly improves the training speed and reduces the running memory and number of model parameters and maintains a certain accuracy on a lager dataset. By redesigning the MNEBlock module and using CBR standard convolution to reduce computation, we integrated the CA (coordinate attention) mechanism to ensure better detection performance. We achieved 94.7% precision, a 2.2 M model size, and a 0.91 M parameter quantity on the SSDD dataset. Full article

(This article belongs to the Section Intelligent Sensors)

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15 pages, 23769 KiB

Open AccessArticle

Generation of Prior Information in a Dual-Mode Microwave-Ultrasound Breast Imaging System

by Hannah Fogel, Max Hughson, Mohammad Asefi, Ian Jeffrey and Joe LoVetri

Sensors 2022, 22(18), 7087; https://doi.org/10.3390/s22187087 - 19 Sep 2022

Cited by 4 | Viewed by 2017

Abstract

A new breast imaging system capable of obtaining ultrasound and microwave scattered-field measurements with minimal or no movement of the breast between measurements has recently been reported. In this work, we describe the methodology that has been developed to generate prior information about [...] Read more.

A new breast imaging system capable of obtaining ultrasound and microwave scattered-field measurements with minimal or no movement of the breast between measurements has recently been reported. In this work, we describe the methodology that has been developed to generate prior information about the internal structures of the breast based on ultrasound data measured with the dual-mode system. This prior information, estimating both the geometry and complex-valued permittivity of tissues within the breast, is incorporated into the microwave inversion algorithm as a means of enhancing image quality. Several techniques to map reconstructed ultrasound speed to complex-valued relative permittivity are investigated. Quantitative images of two simplified dual-mode breast phantoms obtained using experimental data and the various forms of prior information are presented. Though preliminary, the results presented herein provide an understanding of the impacts of different forms of prior information on dual-mode reconstructions of the breast and can be used to inform future work on the subject. Full article

(This article belongs to the Special Issue Advances in Medical Microwave Imaging and Signal Processing, and Hyperthermic Technologies for Healthcare)

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24 pages, 7508 KiB

Open AccessArticle

Weak and Maneuvering Target Detection with Long Observation Time Based on Segment Fusion for Narrowband Radar

by Shaopeng Wei, Yan Dai and Qiang Zhang

Sensors 2022, 22(18), 7086; https://doi.org/10.3390/s22187086 - 19 Sep 2022

Cited by 2 | Viewed by 2291

Abstract

Detecting high-speed and maneuvering targets is challenging in early warning radar applications. Modern early warning radar has many functions such as detection, tracking, imaging, and recognition which need a high signal-to-noise ratio (SNR). Thus, long-time coherent integration is a necessary method to realize [...] Read more.

Detecting high-speed and maneuvering targets is challenging in early warning radar applications. Modern early warning radar has many functions such as detection, tracking, imaging, and recognition which need a high signal-to-noise ratio (SNR). Thus, long-time coherent integration is a necessary method to realize high SNR requirements. However, high-speed and maneuverable motion cause range and Doppler migration, which brings about serious coherent integration loss. Traditional integration methods usually have the drawbacks of model mismatching and high computational complexity. This paper establishes a novel long coherent processing interval (CPI) integration algorithm that detects maneuvering and weak targets which have a low reflection cross-section (RCS) and low echo SNR. The range and Doppler migration problems are solved via a layer integration by blending the association in a tracking-before-detection (TBD) technique. Compact SNR gain is achieved with a target information transmission mechanism and an updated constant false alarm ratio (CFAR) threshold. The algorithm is applicable in multiple target scenarios by considering different velocity ambiguities and maneuvers. A simulation and real-measured experiments confirm the effectiveness of the algorithm. Full article

(This article belongs to the Special Issue Airborne Distributed Radar Technology)

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19 pages, 741 KiB

Open AccessArticle

JDAPCOO: Resource Scheduling and Energy Efficiency Optimization in 5G and Satellite Converged Networks for Power Transmission and Distribution Scenarios

by Sachula Meng, Sicheng Zhu, Zhihui Wang, Ruibing Zhang, Jinxia Han, Jiayan Liu, Haoran Sun, Peng Qin and Xiongwen Zhao

Sensors 2022, 22(18), 7085; https://doi.org/10.3390/s22187085 - 19 Sep 2022

Cited by 4 | Viewed by 2246

Abstract

Along with the continuous revolution of energy production and energy consumption structures, the information data of smart grids have exploded, and effective solutions are urgently needed to solve the problem of power devices resource scheduling and energy efficiency optimization. In this paper, we [...] Read more.

Along with the continuous revolution of energy production and energy consumption structures, the information data of smart grids have exploded, and effective solutions are urgently needed to solve the problem of power devices resource scheduling and energy efficiency optimization. In this paper, we propose a fifth generation (5G) and satellite converged network architecture for power transmission and distribution scenarios, where power transmission and distribution devices (PDs) can choose to forward power data to a cloud server data center via ground networks or space-based networks for power grid regulation and control. We propose a Joint Device Association and Power Control Online Optimization (JDAPCOO) algorithm to maximize the long-term system energy efficiency while guaranteeing the minimum transmission rate requirement of PDs. Since the formulated issue is a mixed integer nonconvex optimization problem with high complexity, we decompose the original problem into two subproblems, i.e., device association and power control, which are solved using a genetic algorithm and improved simulated annealing algorithm, respectively. Numerical simulation results show that when the number of PDs is 50, the proposed algorithm can improve the system energy efficiency by 105%, 545.05% and 835.26%, respectively, compared with the equal power allocation algorithm, random power allocation algorithm and random device association algorithm. Full article

(This article belongs to the Special Issue Satellite Based IoT Networks for Emerging Applications)

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22 pages, 3172 KiB

Open AccessArticle

A Prediction Model of Defecation Based on BP Neural Network and Bowel Sound Signal Features

by Tie Zhang, Zequan Huang, Yanbiao Zou, Jun Zhao and Yuwei Ke

Sensors 2022, 22(18), 7084; https://doi.org/10.3390/s22187084 - 19 Sep 2022

Cited by 3 | Viewed by 2480

Abstract

(1) Background: Incontinence and its complications pose great difficulties in the care of the disabled. Currently, invasive incontinence monitoring methods are too invasive, expensive, and bulky to be widely used. Compared with previous methods, bowel sound monitoring is the most commonly used non-invasive [...] Read more.

(1) Background: Incontinence and its complications pose great difficulties in the care of the disabled. Currently, invasive incontinence monitoring methods are too invasive, expensive, and bulky to be widely used. Compared with previous methods, bowel sound monitoring is the most commonly used non-invasive monitoring method for intestinal diseases and may even provide clinical support for doctors. (2) Methods: This paper proposes a method based on the features of bowel sound signals, which uses a BP classification neural network to predict bowel defecation and realizes a non-invasive collection of physiological signals. Firstly, according to the physiological function of human defecation, bowel sound signals were selected for monitoring and analysis before defecation, and a portable non-invasive bowel sound collection system was built. Then, the detector algorithm based on iterative kurtosis and the signal processing method based on Kalman filter was used to process the signal to remove the aliasing noise in the bowel sound signal, and feature extraction was carried out in the time domain, frequency domain, and time–frequency domain. Finally, BP neural network was selected to build a classification training method for the features of bowel sound signals. (3) Results: Experimental results based on real data sets show that the proposed method can converge to a stable state and achieve a prediction accuracy of 88.71% in 232 records, which is better than other classification methods. (4) Conclusions: The result indicates that the proposed method could provide a high-precision defecation prediction result for patients with fecal incontinence, so as to prepare for defecation in advance. Full article

(This article belongs to the Special Issue E-health System Based on Sensors and Artificial Intelligence)

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17 pages, 10105 KiB

Open AccessArticle

Joint Estimation for Time Delay and Direction of Arrival in Reconfigurable Intelligent Surface with OFDM

by Jinzhi Du, Weijia Cui, Bin Ba, Chunxiao Jian and Liye Zhang

Sensors 2022, 22(18), 7083; https://doi.org/10.3390/s22187083 - 19 Sep 2022

Cited by 1 | Viewed by 2119

Abstract

Recently, the joint estimation for time delay (TD) and direction of arrival (DOA) has suffered from the high complexity of processing multi-dimensional signal models and the ineffectiveness of correlated/coherent signals. In order to improve this situation, a joint estimation method using orthogonal frequency [...] Read more.

Recently, the joint estimation for time delay (TD) and direction of arrival (DOA) has suffered from the high complexity of processing multi-dimensional signal models and the ineffectiveness of correlated/coherent signals. In order to improve this situation, a joint estimation method using orthogonal frequency division multiplexing (OFDM) and a uniform planar array composed of reconfigurable intelligent surface (RIS) is proposed. First, the time-domain coding function of the RIS is combined with the multi-carrier characteristic of the OFDM signal to construct the coded channel frequency response in tensor form. Then, the coded channel frequency response covariance matrix is decomposed by CANDECOMP/PARAFAC (CPD) to separate the signal subspaces of TD and DOA. Finally, we perform a one-dimensional (1D) spectral search for TD values and a two-dimensional (2D) spectral search for DOA values. Compared to previous efforts, this algorithm not only enhances the adaptability of coherent signals, but also greatly decreases the complexity. Simulation results indicate the robustness and effectiveness for the proposed algorithm in independent, coherent, and mixed multipath environments and low signal-to-noise ratio (SNR) conditions. Full article

(This article belongs to the Topic Advances in Array Signal Processing with Errors: Models, Algorithms and Applications)

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15 pages, 2071 KiB

Open AccessArticle

Development of a Novel Approach for Detection of Equine Lameness Based on Inertial Sensors: A Preliminary Study

by Cristian Mihaita Crecan, Iancu Adrian Morar, Alexandru Florin Lupsan, Calin Cosmin Repciuc, Mirela Alexandra Rus and Cosmin Petru Pestean

Sensors 2022, 22(18), 7082; https://doi.org/10.3390/s22187082 - 19 Sep 2022

Cited by 2 | Viewed by 2951

Abstract

Both as an aid for less experienced clinicians and to enhance objectivity and sharp clinical skills in professionals, quantitative technologies currently bring the equine lameness diagnostic closer to evidence-based veterinary medicine. The present paper describes an original, inertial sensor-based wireless device system, the [...] Read more.

Both as an aid for less experienced clinicians and to enhance objectivity and sharp clinical skills in professionals, quantitative technologies currently bring the equine lameness diagnostic closer to evidence-based veterinary medicine. The present paper describes an original, inertial sensor-based wireless device system, the Lameness Detector 0.1, used in ten horses with different lameness degrees in one fore- or hind-leg. By recording the impulses on three axes of the incorporated accelerometer in each leg of the assessed horse, and then processing the data using custom-designed software, the device proved its usefulness in lameness identification and severity scoring. Mean impulse values on the horizontal axis calculated for five consecutive steps above 85, regardless of the leg, indicated the slightest subjectively recognizable lameness, increasing to 130 in severe gait impairment. The range recorded on the same axis (between 61.2 and 67.4) in the sound legs allowed a safe cut-off value of 80 impulses for diagnosing a painful limb. The significance of various comparisons and several correlations highlighted the potential of this simple, affordable, and easy-to-use lameness detector device for further standardization as an aid for veterinarians in diagnosing lameness in horses. Full article

(This article belongs to the Section Intelligent Sensors)

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16 pages, 3127 KiB

Open AccessArticle

Blind Quality Prediction for View Synthesis Based on Heterogeneous Distortion Perception

by Haozhi Shi, Lanmei Wang and Guibao Wang

Sensors 2022, 22(18), 7081; https://doi.org/10.3390/s22187081 - 19 Sep 2022

Cited by 3 | Viewed by 1768

Abstract

The quality of synthesized images directly affects the practical application of virtual view synthesis technology, which typically uses a depth-image-based rendering (DIBR) algorithm to generate a new viewpoint based on texture and depth images. Current view synthesis quality metrics commonly evaluate the quality [...] Read more.

The quality of synthesized images directly affects the practical application of virtual view synthesis technology, which typically uses a depth-image-based rendering (DIBR) algorithm to generate a new viewpoint based on texture and depth images. Current view synthesis quality metrics commonly evaluate the quality of DIBR-synthesized images, where the DIBR process is computationally expensive and time-consuming. In addition, the existing view synthesis quality metrics cannot achieve robustness due to the shallow hand-crafted features. To avoid the complicated DIBR process and learn more efficient features, this paper presents a blind quality prediction model for view synthesis based on HEterogeneous DIstortion Perception, dubbed HEDIP, which predicts the image quality of view synthesis from texture and depth images. Specifically, the texture and depth images are first fused based on discrete cosine transform to simulate the distortion of view synthesis images, and then the spatial and gradient domain features are extracted in a Two-Channel Convolutional Neural Network (TCCNN). Finally, a fully connected layer maps the extracted features to a quality score. Notably, the ground-truth score of the source image cannot effectively represent the labels of each image patch during training due to the presence of local distortions in view synthesis image. So, we design a Heterogeneous Distortion Perception (HDP) module to provide effective training labels for each image patch. Experiments show that with the help of the HDP module, the proposed model can effectively predict the quality of view synthesis. Experimental results demonstrate the effectiveness of the proposed model. Full article

(This article belongs to the Section Intelligent Sensors)

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17 pages, 62326 KiB

Open AccessArticle

Denoising Single Images by Feature Ensemble Revisited

by Masud An Nur Islam Fahim, Nazmus Saqib, Shafkat Khan Siam and Ho Yub Jung

Sensors 2022, 22(18), 7080; https://doi.org/10.3390/s22187080 - 19 Sep 2022

Cited by 3 | Viewed by 2309

Abstract

Image denoising is still a challenging issue in many computer vision subdomains. Recent studies have shown that significant improvements are possible in a supervised setting. However, a few challenges, such as spatial fidelity and cartoon-like smoothing, remain unresolved or decisively overlooked. Our study [...] Read more.

Image denoising is still a challenging issue in many computer vision subdomains. Recent studies have shown that significant improvements are possible in a supervised setting. However, a few challenges, such as spatial fidelity and cartoon-like smoothing, remain unresolved or decisively overlooked. Our study proposes a simple yet efficient architecture for the denoising problem that addresses the aforementioned issues. The proposed architecture revisits the concept of modular concatenation instead of long and deeper cascaded connections, to recover a cleaner approximation of the given image. We find that different modules can capture versatile representations, and a concatenated representation creates a richer subspace for low-level image restoration. The proposed architecture’s number of parameters remains smaller than in most of the previous networks and still achieves significant improvements over the current state-of-the-art networks. Full article

(This article belongs to the Special Issue Image Denoising and Image Super-resolution for Sensing Application)

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17 pages, 6644 KiB

Open AccessEditor’s ChoiceArticle

Dynamic Path Planning for Forklift AGV Based on Smoothing A* and Improved DWA Hybrid Algorithm

by Bin Wu, Xiaonan Chi, Congcong Zhao, Wei Zhang, Yi Lu and Di Jiang

Sensors 2022, 22(18), 7079; https://doi.org/10.3390/s22187079 - 19 Sep 2022

Cited by 52 | Viewed by 5097

Abstract

FAGV is a kind of heavy equipment in the storage environment. Its path needs to be simple and smooth and should be able to avoid sudden obstacles in the process of driving. According to the environmental characteristics of intelligent storage and the task [...] Read more.

FAGV is a kind of heavy equipment in the storage environment. Its path needs to be simple and smooth and should be able to avoid sudden obstacles in the process of driving. According to the environmental characteristics of intelligent storage and the task requirements of FAGV, this paper proposed a hybrid dynamic path planning algorithm for FAGV based on improved A* and improved DWA. The improved A* algorithm can plan the global optimal path more suitable for FAGV. The improved evaluation function of DWA can ensure that the local path of FAGV is closer to the global path. DWA combines the rolling window method for local path planning to avoid sudden unknown static and dynamic obstacles. In addition, this paper verifies the effectiveness of the algorithm through simulation. The simulation results show that the algorithm can avoid obstacles dynamically without being far away from the global optimal path. Full article

(This article belongs to the Special Issue Automated Guided Vehicle Integrated with Collaborative Robot)

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17 pages, 7453 KiB

Open AccessArticle

Stability Analysis of Two Power Converters Control Algorithms Connected to Micro-Grids with Wide Frequency Variation

by Jaime Rohten, Felipe Villarroel, Esteban Pulido, Javier Muñoz, José Silva and Marcelo Perez

Sensors 2022, 22(18), 7078; https://doi.org/10.3390/s22187078 - 19 Sep 2022

Cited by 5 | Viewed by 2138

Abstract

Distributed power generation, micro-grids, and networks working in islanding mode have strong deviations in voltage quantities. These deviations can be divided into amplitude and frequency. Amplitude deviations are well-known and studied, as they are common in small and big grids. However, deviations on [...] Read more.

Distributed power generation, micro-grids, and networks working in islanding mode have strong deviations in voltage quantities. These deviations can be divided into amplitude and frequency. Amplitude deviations are well-known and studied, as they are common in small and big grids. However, deviations on the ac mains frequency have not been widely studied. The literature shows control schemes capable of bearing these variations, but no systematic analysis has been performed to ensure stability. As the majority of power converters are designed for big grids, their analysis and design neglect frequency disturbances, therefore those devices allow a very small frequency operating window. For instance, in power converters that need to be synchronized to the grid, the standard deviation does not go beyond 0.5 Hz, and for grid-tied inverters it does not go beyond 1 Hz, whereas variations of around 8 Hz can be expected in micro-grids. This work presents a comprehensive analysis of the control system’s stability, where two different control schemes for a back-to-back static converter topology are implemented and studied under a wide variable grid frequency. Because the behavior of power converters is nonlinear and coupled, dynamic and static decouplers are usually introduced in the controller, being a key element on the scheme according to the findings. The results show that using just a static decoupler does not guarantee stability under frequency variations; meanwhile, when a dynamic decoupler is used, the operating window can be greatly extended. The procedure shown in this paper can also be extended to other control algorithms, making it possible to carefully choose the control system for a variable frequency condition. Simulated and experimental results confirm the theoretical approach. Full article

(This article belongs to the Special Issue Measurements, Predictions, and Control in Microgrids and Power Electronic System)

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16 pages, 4973 KiB

Open AccessArticle

Global Vibration Comfort Evaluation of Footbridges Based on Computer Vision

by Jianxiu Hu, Qiankun Zhu and Qiong Zhang

Sensors 2022, 22(18), 7077; https://doi.org/10.3390/s22187077 - 19 Sep 2022

Cited by 1 | Viewed by 2112

Abstract

The vibration comfort evaluation is a control standard other than strength and deflection, but the general comfort evaluation method only considers the response of the mid-span position and does not consider the difference in the vibration response of different positions at the same [...] Read more.

The vibration comfort evaluation is a control standard other than strength and deflection, but the general comfort evaluation method only considers the response of the mid-span position and does not consider the difference in the vibration response of different positions at the same time. It is crucial to study how pedestrians actually feel when they walk on footbridges. The computer vision-based vibration comfort evaluation method is a novel method with advantages, such as noncontact and long-distance. In this study, a computer vision-based method was used to evaluate the global vibration comfort of footbridges under human-induced excitation. The improved Lucas–Kanade optical flow method is used for multitarget displacement identification of footbridges. Additionally, the YOLOv5 algorithm for pedestrian detection is used to obtain the position information of pedestrians on the footbridges. Then, according to the pedestrian position information, the structural responses of different pedestrian positions corresponding to time periods are extracted from the displacement responses of each point, and they are combined to obtain the structural global displacement. The global acceleration can be obtained by calculating the global displacement. The rms value can be calculated based on the global acceleration and compared with the standard for comfort evaluation. The global comfort evaluation method is validated by pedestrian walking experiments with different frequencies on a laboratory footbridge. The experimental results show that the computer vision-based global comfort evaluation method for footbridges is feasible and is a more specific and real-time comfort evaluation method. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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49 pages, 19636 KiB

Open AccessEditor’s ChoiceReview

Sensing with Femtosecond Laser Filamentation

by Pengfei Qi, Wenqi Qian, Lanjun Guo, Jiayun Xue, Nan Zhang, Yuezheng Wang, Zhi Zhang, Zeliang Zhang, Lie Lin, Changlin Sun, Liguo Zhu and Weiwei Liu

Sensors 2022, 22(18), 7076; https://doi.org/10.3390/s22187076 - 19 Sep 2022

Cited by 27 | Viewed by 7597

Abstract

Femtosecond laser filamentation is a unique nonlinear optical phenomenon when high-power ultrafast laser propagation in all transparent optical media. During filamentation in the atmosphere, the ultrastrong field of 10¹³–10¹⁴ W/cm² with a large distance ranging from meter to kilometers [...] Read more.

Femtosecond laser filamentation is a unique nonlinear optical phenomenon when high-power ultrafast laser propagation in all transparent optical media. During filamentation in the atmosphere, the ultrastrong field of 10¹³–10¹⁴ W/cm² with a large distance ranging from meter to kilometers can effectively ionize, break, and excite the molecules and fragments, resulting in characteristic fingerprint emissions, which provide a great opportunity for investigating strong-field molecules interaction in complicated environments, especially remote sensing. Additionally, the ultrastrong intensity inside the filament can damage almost all the detectors and ignite various intricate higher order nonlinear optical effects. These extreme physical conditions and complicated phenomena make the sensing and controlling of filamentation challenging. This paper mainly focuses on recent research advances in sensing with femtosecond laser filamentation, including fundamental physics, sensing and manipulating methods, typical filament-based sensing techniques and application scenarios, opportunities, and challenges toward the filament-based remote sensing under different complicated conditions. Full article

(This article belongs to the Special Issue Sensing with Femtosecond Laser Filamentation)

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24 pages, 831 KiB

Open AccessArticle

PUFTAP-IoT: PUF-Based Three-Factor Authentication Protocol in IoT Environment Focused on Sensing Devices

by JoonYoung Lee, JiHyeon Oh, DeokKyu Kwon, MyeongHyun Kim, SungJin Yu, Nam-Su Jho and Youngho Park

Sensors 2022, 22(18), 7075; https://doi.org/10.3390/s22187075 - 19 Sep 2022

Cited by 17 | Viewed by 2989

Abstract

In IoT-based environments, smart services can be provided to users under various environments, such as smart homes, smart factories, smart cities, smart transportation, and healthcare, by utilizing sensing devices. Nevertheless, a series of security problems may arise because of the nature of the [...] Read more.

In IoT-based environments, smart services can be provided to users under various environments, such as smart homes, smart factories, smart cities, smart transportation, and healthcare, by utilizing sensing devices. Nevertheless, a series of security problems may arise because of the nature of the wireless channel in the Wireless Sensor Network (WSN) for utilizing IoT services. Authentication and key agreements are essential elements for providing secure services in WSNs. Accordingly, two-factor and three-factor-based authentication protocol research is being actively conducted. However, IoT service users can be vulnerable to ID/password pair guessing attacks by setting easy-to-remember identities and passwords. In addition, sensors and sensing devices deployed in IoT environments are vulnerable to capture attacks. To address this issue, in this paper, we analyze the protocols of Chunka et al., Amintoosi et al., and Hajian et al. and describe their security vulnerabilities. Moreover, this paper introduces PUF and honey list techniques with three-factor authentication to design protocols resistant to ID/password pair guessing, brute-force, and capture attacks. Accordingly, we introduce PUFTAP-IoT, which can provide secure services in the IoT environment. To prove the security of PUFTAP-IoT, we perform formal analyses through Burrows Abadi Needham (BAN) logic, Real-Or-Random (ROR) model, and scyther simulation tools. In addition, we demonstrate the efficiency of the protocol compared with other authentication protocols in terms of security, computational cost, and communication cost, showing that it can provide secure services in IoT environments. Full article

(This article belongs to the Collection Wireless Sensor Networks towards the Internet of Things)

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27 pages, 13138 KiB

Open AccessArticle

A Tunable Hyperspectral Imager for Detection and Quantification of Marine Biofouling on Coated Surfaces

by Joaquim Santos, Morten Lysdahlgaard Pedersen, Burak Ulusoy, Claus Erik Weinell, Henrik Chresten Pedersen, Paul Michael Petersen, Kim Dam-Johansen and Christian Pedersen

Sensors 2022, 22(18), 7074; https://doi.org/10.3390/s22187074 - 19 Sep 2022

Cited by 7 | Viewed by 3195

Abstract

Fouling control coatings (FCCs) are used to prevent the accumulation of marine biofouling on, e.g., ship hulls, which causes increased fuel consumption and the global spread of non-indigenous species. The standards for performance evaluations of FCCs rely on visual inspections, which induce a [...] Read more.

Fouling control coatings (FCCs) are used to prevent the accumulation of marine biofouling on, e.g., ship hulls, which causes increased fuel consumption and the global spread of non-indigenous species. The standards for performance evaluations of FCCs rely on visual inspections, which induce a degree of subjectivity. The use of RGB images for objective evaluations has already received interest from several authors, but the limited acquired information restricts detailed analyses class-wise. This study demonstrates that hyperspectral imaging (HSI) expands the specificity of biofouling assessments of FCCs by capturing distinguishing spectral features. We developed a staring-type hyperspectral imager using a liquid crystal tunable filter as the wavelength selective element. A novel light-emitting diode illumination system with high and uniform irradiance was designed to compensate for the low-filter transmittance. A spectral library was created from reflectance-calibrated optical signatures of representative biofouling species and coated panels. We trained a neural network on the annotated library to assign a class to each pixel. The model was evaluated on an artificially generated target, and global accuracy of 95% was estimated. The classifier was tested on coated panels (exposed at the CoaST Maritime Test Centre) with visible intergrown biofouling. The segmentation results were used to determine the coverage percentage per class. Although a detailed taxonomic description might be complex due to spectral similarities among groups, these results demonstrate the feasibility of HSI for repeatable and quantifiable biofouling detection on coated surfaces. Full article

(This article belongs to the Section Sensing and Imaging)

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20 pages, 4172 KiB

Open AccessArticle

Enhanced Convolutional Neural Network for In Situ AUV Thruster Health Monitoring Using Acoustic Signals

by Sang-Jae Yeo, Woen-Sug Choi, Suk-Yoon Hong and Jee-Hun Song

Sensors 2022, 22(18), 7073; https://doi.org/10.3390/s22187073 - 19 Sep 2022

Cited by 12 | Viewed by 2072

Abstract

As the demand for ocean exploration increases, studies are being actively conducted on autonomous underwater vehicles (AUVs) that can efficiently perform various missions. To successfully perform long-term, wide-ranging missions, it is necessary to apply fault diagnosis technology to AUVs. In this study, a [...] Read more.

As the demand for ocean exploration increases, studies are being actively conducted on autonomous underwater vehicles (AUVs) that can efficiently perform various missions. To successfully perform long-term, wide-ranging missions, it is necessary to apply fault diagnosis technology to AUVs. In this study, a system that can monitor the health of in situ AUV thrusters using a convolutional neural network (CNN) was developed. As input data, an acoustic signal that comprehensively contains the mechanical and hydrodynamic information of the AUV thruster was adopted. The acoustic signal was pre-processed into two-dimensional data through continuous wavelet transform. The neural network was trained with three different pre-processing methods and the accuracy was compared. The decibel scale was more effective than the linear scale, and the normalized decibel scale was more effective than the decibel scale. Through tests on off-training conditions that deviate from the neural network learning condition, the developed system properly recognized the distribution characteristics of noise sources even when the operating speed and the thruster rotation speed changed, and correctly diagnosed the state of the thruster. These results showed that the acoustic signal-based CNN can be effectively used for monitoring the health of the AUV’s thrusters. Full article

(This article belongs to the Special Issue Underwater Robotics in 2022-2023)

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27 pages, 22568 KiB

Open AccessArticle

Deep Learning with LPC and Wavelet Algorithms for Driving Fault Diagnosis

by Cihun-Siyong Alex Gong, Chih-Hui Simon Su, Yuan-En Liu, De-Yu Guu and Yu-Hua Chen

Sensors 2022, 22(18), 7072; https://doi.org/10.3390/s22187072 - 19 Sep 2022

Cited by 5 | Viewed by 3465

Abstract

Vehicle fault detection and diagnosis (VFDD) along with predictive maintenance (PdM) are indispensable for early diagnosis in order to prevent severe accidents due to mechanical malfunction in urban environments. This paper proposes an early voiceprint driving fault identification system using machine learning algorithms [...] Read more.

Vehicle fault detection and diagnosis (VFDD) along with predictive maintenance (PdM) are indispensable for early diagnosis in order to prevent severe accidents due to mechanical malfunction in urban environments. This paper proposes an early voiceprint driving fault identification system using machine learning algorithms for classification. Previous studies have examined driving fault identification, but less attention has focused on using voiceprint features to locate corresponding faults. This research uses 43 different common vehicle mechanical malfunction condition voiceprint signals to construct the dataset. These datasets were filtered by linear predictive coefficient (LPC) and wavelet transform(WT). After the original voiceprint fault sounds were filtered and obtained the main fault characteristics, the deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) architectures are used for identification. The experimental results show that the accuracy of the CNN algorithm is the best for the LPC dataset. In addition, for the wavelet dataset, DNN has the best performance in terms of identification performance and training time. After cross-comparison of experimental results, the wavelet algorithm combined with DNN can improve the identification accuracy by up to 16.57% compared with other deep learning algorithms and reduce the model training time by up to 21.5% compared with other algorithms. Realizing the cross-comparison of recognition results through various machine learning methods, it is possible for the vehicle to proactively remind the driver of the real-time potential hazard of vehicle machinery failure. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors Section 2022)

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15 pages, 4711 KiB

Open AccessArticle

Aeroengine Working Condition Recognition Based on MsCNN-BiLSTM

by Jinsong Zheng, Jingbo Peng, Weixuan Wang and Shuaiguo Li

Sensors 2022, 22(18), 7071; https://doi.org/10.3390/s22187071 - 19 Sep 2022

Cited by 2 | Viewed by 1884

Abstract

Aeroengine working condition recognition is a pivotal step in engine fault diagnosis. Currently, most research on aeroengine condition recognition focuses on the stable condition. To identify the aeroengine working conditions including transition conditions and better achieve the fault diagnosis of engines, a recognition [...] Read more.

Aeroengine working condition recognition is a pivotal step in engine fault diagnosis. Currently, most research on aeroengine condition recognition focuses on the stable condition. To identify the aeroengine working conditions including transition conditions and better achieve the fault diagnosis of engines, a recognition method based on the combination of multi-scale convolutional neural networks (MsCNNs) and bidirectional long short-term memory neural networks (BiLSTM) is proposed. Firstly, the MsCNN is used to extract the multi-scale features from the flight data. Subsequently, the spatial and channel weights are corrected using the weight adaptive correction module. Then, the BiLSTM is used to extract the temporal dependencies in the data. The Focal Loss is used as the loss function to improve the recognition ability of the model for confusable samples. L2 regularization and DropOut strategies are employed to prevent overfitting. Finally, the established model is used to identify the working conditions of an engine sortie, and the recognition results of different models are compared. The overall recognition accuracy of the proposed model reaches over 97%, and the recognition accuracy of transition conditions reaches 94%. The results show that the method based on MsCNN–BiLSTM can effectively identify the aeroengine working conditions including transition conditions accurately. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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31 pages, 2297 KiB

Open AccessReview

A Systematic Literature Review of Predictive Maintenance for Defence Fixed-Wing Aircraft Sustainment and Operations

by Michael J. Scott, Wim J. C. Verhagen, Marie T. Bieber and Pier Marzocca

Sensors 2022, 22(18), 7070; https://doi.org/10.3390/s22187070 - 19 Sep 2022

Cited by 31 | Viewed by 11019

Abstract

In recent decades, the increased use of sensor technologies, as well as the increase in digitalisation of aircraft sustainment and operations, have enabled capabilities to detect, diagnose, and predict the health of aircraft structures, systems, and components. Predictive maintenance and closely related concepts, [...] Read more.

In recent decades, the increased use of sensor technologies, as well as the increase in digitalisation of aircraft sustainment and operations, have enabled capabilities to detect, diagnose, and predict the health of aircraft structures, systems, and components. Predictive maintenance and closely related concepts, such as prognostics and health management (PHM) have attracted increasing attention from a research perspective, encompassing a growing range of original research papers as well as review papers. When considering the latter, several limitations remain, including a lack of research methodology definition, and a lack of review papers on predictive maintenance which focus on military applications within a defence context. This review paper aims to address these gaps by providing a systematic two-stage review of predictive maintenance focused on a defence domain context, with particular focus on the operations and sustainment of fixed-wing defence aircraft. While defence aircraft share similarities with civil aviation platforms, defence aircraft exhibit significant variation in operations and environment and have different performance objectives and constraints. The review utilises a systematic methodology incorporating bibliometric analysis of the considered domain, as well as text processing and clustering of a set of aligned review papers to position the core topics for subsequent discussion. This discussion highlights state-of-the-art applications and associated success factors in predictive maintenance and decision support, followed by an identification of practical and research challenges. The scope is primarily confined to fixed-wing defence aircraft, including legacy and emerging aircraft platforms. It highlights that challenges in predictive maintenance and PHM for researchers and practitioners alike do not necessarily revolve solely on what can be monitored, but also covers how robust decisions can be made with the quality of data available. Full article

(This article belongs to the Special Issue Sensing Technologies for Fault Diagnostics and Prognosis)

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