Sensors

Research

19 pages, 5032 KiB

Open AccessArticle

Research on a Fault Diagnosis Method for Crankshafts Based on Improved Multi-Scale Permutation Entropy

by Fengfeng Bie, Yu Shu, Fengxia Lyu, Xuedong Liu, Yi Lu, Qianqian Li, Hanyang Zhang and Xueping Ding

Sensors 2024, 24(3), 726; https://doi.org/10.3390/s24030726 - 23 Jan 2024

Viewed by 780

Abstract

As the crucial part of a transmission assembly, the monitoring of the status of the crankshaft is essential for the normal working of a reciprocating machinery system. In consideration of the interaction between crankshaft system components, the fault vibration feature is typically non-stationary [...] Read more.

As the crucial part of a transmission assembly, the monitoring of the status of the crankshaft is essential for the normal working of a reciprocating machinery system. In consideration of the interaction between crankshaft system components, the fault vibration feature is typically non-stationary and nonlinear, and the single-scale feature extraction method cannot adequately assess the fault features, therefore a novel impact feature extraction method based on genetic algorithms to optimize multi-scale permutation entropy is proposed. Compared with other traditional feature extraction methods, the proposed method illustrates good robustness and high adaptability in the signal processing of crankshaft vibrations. Firstly, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method is developed on the signal to obtain several intrinsic mode function (IMF) components, and the IMF components with a large kurtosis are selected for array reorganization. Then, the parameters of multi-scale permutation entropy (MPE) are optimized based on genetic algorithm (GA), the multi-scale permutation entropy is calculated and the feature vector set is constructed. The feature vector set is input into the support vector machine (SVM) and optimized by a particle swarm optimization (PSO) model for training and final pattern recognition, where the Variational Mode Decomposition(VMD)-GA-MPE with a PSO-SVM recognition model and the ICEEMDAN-MPE with PSO-SVM recognition model without GA optimization are constructed for a comparison with the proposed method. The research result illustrates that the proposed method, which inputs the genetic algorithm optimized multi-scale permutation entropy extracted from the ICEEMDAN decomposition into the PSO-SVM, performs well in impact feature extraction and the pattern recognition of crankshaft vibrations. Full article

(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis)

► Show Figures

Figure 1

22 pages, 1010 KiB

Open AccessArticle

Enhancing Aircraft Safety through Advanced Engine Health Monitoring with Long Short-Term Memory

by Suleyman Yildirim and Zeeshan A. Rana

Sensors 2024, 24(2), 518; https://doi.org/10.3390/s24020518 - 14 Jan 2024

Viewed by 985

Abstract

Predictive maintenance holds a crucial role in various industries such as the automotive, aviation and factory automation industries when it comes to expensive engine upkeep. Predicting engine maintenance intervals is vital for devising effective business management strategies, enhancing occupational safety and optimising efficiency. [...] Read more.

Predictive maintenance holds a crucial role in various industries such as the automotive, aviation and factory automation industries when it comes to expensive engine upkeep. Predicting engine maintenance intervals is vital for devising effective business management strategies, enhancing occupational safety and optimising efficiency. To achieve predictive maintenance, engine sensor data are harnessed to assess the wear and tear of engines. In this research, a Long Short-Term Memory (LSTM) architecture was employed to forecast the remaining lifespan of aircraft engines. The LSTM model was evaluated using the NASA Turbofan Engine Corruption Simulation dataset and its performance was benchmarked against alternative methodologies. The results of these applications demonstrated exceptional outcomes, with the LSTM model achieving the highest classification accuracy at 98.916% and the lowest mean average absolute error at 1.284%. Full article

(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis)

► Show Figures

Figure 1

17 pages, 732 KiB

Open AccessArticle

Cost-Sensitive Decision Support for Industrial Batch Processes

by Simon Mählkvist, Jesper Ejenstam and Konstantinos Kyprianidis

Sensors 2023, 23(23), 9464; https://doi.org/10.3390/s23239464 - 28 Nov 2023

Viewed by 601

Abstract

In this work, cost-sensitive decision support was developed. Using Batch Data Analytics (BDA) methods of the batch data structure and feature accommodation, the batch process property and sensor data can be accommodated. The batch data structure organises the batch processes’ data, and the [...] Read more.

In this work, cost-sensitive decision support was developed. Using Batch Data Analytics (BDA) methods of the batch data structure and feature accommodation, the batch process property and sensor data can be accommodated. The batch data structure organises the batch processes’ data, and the feature accommodation approach derives statistics from the time series, consequently aligning the time series with the other features. Three machine learning classifiers were implemented for comparison: Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Machine (SVM). It is possible to filter out the low-probability predictions by leveraging the classifiers’ probability estimations. Consequently, the decision support has a trade-off between accuracy and coverage. Cost-sensitive learning was used to implement a cost matrix, which further aggregates the accuracy–coverage trade into cost metrics. Also, two scenarios were implemented for accommodating out-of-coverage batches. The batch is discarded in one scenario, and the other is processed. The Random Forest classifier was shown to outperform the other classifiers and, compared to the baseline scenario, had a relative cost of 26%. This synergy of methods provides cost-aware decision support for analysing the intricate workings of a multiprocess batch data system. Full article

(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis)

► Show Figures

Figure 1

24 pages, 7559 KiB

Open AccessArticle

On Model-Based Transfer Learning Method for the Detection of Inter-Turn Short Circuit Faults in PMSM

by Mingsheng Wang, Qiang Song and Wuxuan Lai

Sensors 2023, 23(22), 9145; https://doi.org/10.3390/s23229145 - 13 Nov 2023

Viewed by 847

Abstract

The early detection of an inter-turn short circuit (ITSC) fault is extremely critical for permanent magnet synchronous motors (PMSMs) because it can lead to catastrophic consequences. In this study, a model-based transfer learning method is developed for ITSC fault detection. The contribution can [...] Read more.

The early detection of an inter-turn short circuit (ITSC) fault is extremely critical for permanent magnet synchronous motors (PMSMs) because it can lead to catastrophic consequences. In this study, a model-based transfer learning method is developed for ITSC fault detection. The contribution can be summarized as two points. First of all, a Bayesian-optimized residual dilated CNN model was proposed for the pre-training of the method. The dilated convolution is utilized to extend the receptive domain of the model, the residual architecture is employed to surmount the degradation problems, and the Bayesian optimization method is launched to address the hyperparameters tuning issues. Secondly, a transfer learning framework and strategy are presented to settle the new target domain datasets after the pre-training of the proposed model. Furthermore, motor fault experiments are carried out to validate the effectiveness of the proposed method. Comparison with seven other methods indicates the performance and advantage of the proposed method. Full article

(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis)

► Show Figures

Figure 1

17 pages, 3486 KiB

Open AccessArticle

A New Strategy for Bearing Health Assessment with a Dynamic Interval Prediction Model

by Lingli Jiang, Heshan Sheng, Tongguang Yang, Hujiao Tang, Xuejun Li and Lianbin Gao

Sensors 2023, 23(18), 7696; https://doi.org/10.3390/s23187696 - 6 Sep 2023

Cited by 5 | Viewed by 824

Abstract

Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment’s smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper [...] Read more.

Bearing is the critical basic component of rotating machinery and its remaining life prediction is very important for mechanical equipment’s smooth and healthy operation. However, fast and accurate bearing life prediction has always been a difficult point in industry and academia. This paper proposes a new strategy for bearing health assessment based on a model-driven dynamic interval prediction model. Firstly, the mapping proportion algorithm is used to determine whether the measured data are in the degradation stage. After finding the starting point of prediction, the improved annealing algorithm is used to determine the shortest data interval that can be used for accurate prediction. Then, based on the bearing degradation curve and the information fusion inverse health index, the health index is obtained from 36 general indexes in the time domain and frequency domain through screening, fusion, and inversion. Finally, the state space equation is constructed based on the Paris-DSSM formula and the particle filter is used to iterate the state space equation parameters with the minimum interval data to construct the life prediction model. The proposed method is verified by XJTU-SY rolling bearing life data. The results show that the prediction accuracy of the proposed strategy for the remaining life of the bearing can reach more than 90%. It is verified that the improved simulated annealing algorithm selects limited interval data, reconstructs health indicators based on bearing degradation curve and information fusion, and updates the Paris-DSSM state space equation through the particle filter algorithm. The bearing life prediction model constructed on this basis is accurate and effective. Full article

(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis)

► Show Figures

Figure 1

24 pages, 6496 KiB

Open AccessArticle

The Effects Analysis of Contact Stiffness of Double-Row Tapered Roller Bearing under Composite Loads

by Fanyu Zhang, Hangyuan Lv, Qingkai Han and Mingqi Li

Sensors 2023, 23(10), 4967; https://doi.org/10.3390/s23104967 - 22 May 2023

Cited by 2 | Viewed by 2078

Abstract

Double-row tapered roller bearings have been widely used in various equipment recently due to their compact structure and ability to withstand large loads. The dynamic stiffness is composed of contact stiffness, oil film stiffness and support stiffness, and the contact stiffness has the [...] Read more.

Double-row tapered roller bearings have been widely used in various equipment recently due to their compact structure and ability to withstand large loads. The dynamic stiffness is composed of contact stiffness, oil film stiffness and support stiffness, and the contact stiffness has the most significant influence on the dynamic performance of the bearing. There are few studies on the contact stiffness of double-row tapered roller bearings. Firstly, the contact mechanics calculation model of double-row tapered roller bearing under composite loads has been established. On this basis, the influence of load distribution of double-row tapered roller bearing is analyzed, and the calculation model of contact stiffness of double-row tapered roller bearing is obtained according to the relationship between overall stiffness and local stiffness of bearing. Based on the established stiffness model, the influence of different working conditions on the contact stiffness of the bearing is simulated and analyzed, and the effects of radial load, axial load, bending moment load, speed, preload, and deflection angle on the contact stiffness of double row tapered roller bearings have been revealed. Finally, by comparing the results with Adams simulation results, the error is within 8%, which verifies the validity and accuracy of the proposed model and method. The research content of this paper provides theoretical support for the design of double-row tapered roller bearings and the identification of bearing performance parameters under complex loads. Full article

(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis)

► Show Figures

Figure 1

15 pages, 5011 KiB

Open AccessArticle

Analysis of Mechanical Properties and Fatigue Life of Microturbine Angular Contact Ball Bearings under Eccentric Load Conditions

by Haobo Wang, Hangyuan Lv and Zhong Luo

Sensors 2023, 23(9), 4503; https://doi.org/10.3390/s23094503 - 5 May 2023

Cited by 1 | Viewed by 2180

Abstract

Angular contact ball bearings are common basic components in rotating machinery. During the operation of the bearing, the rolling slips, resulting in contact sliding friction between it and the raceway, which in turn causes wear in the rolling element and increase in the [...] Read more.

Angular contact ball bearings are common basic components in rotating machinery. During the operation of the bearing, the rolling slips, resulting in contact sliding friction between it and the raceway, which in turn causes wear in the rolling element and increase in the radial clearance of the bearing. The increase in clearance also affects the stiffness of the bearing, which in turn affects the natural frequency and fatigue life of the bearing. At present, there are few studies on the influence of bearing wear (variation of clearance) on life. In this paper, the finite element model based on the theory of contact mechanics is established for the angular contact ball bearing with medium- and high-speed rotation, and the mechanical properties and fatigue life influenced by the internal action of the bearing are analyzed. The effects of radial load and deflection angle on the mechanical properties and fatigue life of the bearing are also studied. Based on the analysis results of bearing contact mechanical properties and clearance changes, the calculation method of bearing life under rolling element wear is established. The influence of the variation of clearance and preload clearance on bearing life is analyzed, and the optimal preload is obtained. The research results of this paper can provide a theoretical basis for optimizing the installation of angular contact ball bearings, reasonably determining the service conditions, and prolonging the service life of bearings, which is necessary for engineering practice. Full article

(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis)

► Show Figures

Figure 1

12 pages, 3742 KiB

Open AccessArticle

Development of a Stable Oxygen Sensor Using a 761 nm DFB Laser and Multi-Pass Absorption Spectroscopy for Field Measurements

by Jvqiang Chang, Qixin He and Mengxin Li

Sensors 2023, 23(9), 4274; https://doi.org/10.3390/s23094274 - 25 Apr 2023

Cited by 2 | Viewed by 1559

Abstract

An optical sensor system based on wavelength modulation spectroscopy (WMS) was developed for atmospheric oxygen (O₂) detection. A distributed feedback (DFB) laser with butterfly packaging was used to target the O₂ absorption line at 760.89 nm. A compact multi-pass gas [...] Read more.

An optical sensor system based on wavelength modulation spectroscopy (WMS) was developed for atmospheric oxygen (O₂) detection. A distributed feedback (DFB) laser with butterfly packaging was used to target the O₂ absorption line at 760.89 nm. A compact multi-pass gas cell was employed to increase the effective absorption length to 3.3 m. To ensure the stability and anti-interference capability of the sensor in field measurements, the optical module was fabricated with isolation of ambient light and vibration design. A 1f normalized 2f WMS (WMS-2f/1f) technique was adopted to reduce the effect of laser power drift. In addition, a LabVIEW-based dual-channel lock-in amplifier was developed for harmonic detection, which significantly reduced the sensor volume and cost. The detailed detection principle was described, and a theoretical model was established to verify the effectiveness of the technique. Experiments were carried out to obtain the device’s sensing performances. An Allan deviation analysis yielded a minimum detection limit of 0.054% for 1 s integration time that can be further improved to 0.009% at ~60 s. Finally, the reliability and anti-interference capability of the sensor system were verified by the atmospheric O₂ monitoring. Full article

(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis)

► Show Figures

Figure 1

12 pages, 3555 KiB

Open AccessArticle

Photoacoustic Spectroscopy Combined with Integrated Learning to Identify Soybean Oil with Different Frying Durations

by Hui Luo, Kaiyun Yang, Lili Ji, Lingqi Kong and Wei Lu

Sensors 2023, 23(9), 4247; https://doi.org/10.3390/s23094247 - 25 Apr 2023

Viewed by 1221

Abstract

Soybean oil produces harmful substances after long durations of frying. A rapid and nondestructive identification approach for soybean oil was proposed based on photoacoustic spectroscopy and stacking integrated learning. Firstly, a self-designed photoacoustic spectrometer was built for spectral data collection of soybean oil [...] Read more.

Soybean oil produces harmful substances after long durations of frying. A rapid and nondestructive identification approach for soybean oil was proposed based on photoacoustic spectroscopy and stacking integrated learning. Firstly, a self-designed photoacoustic spectrometer was built for spectral data collection of soybean oil with various frying times. At the same time, the actual free fatty acid content and acid value in soybean oil were measured by the traditional titration experiment, which were the basis for soybean oil quality detection. Next, to eliminate the influence of noise, the spectrum from 1150 cm⁻¹ to 3450 cm⁻¹ was selected to remove noise by ensemble empirical mode decomposition. Then three dimensionality reduction methods of principal component analysis, successive projection algorithm, and competitive adaptive reweighting algorithm were used to reduce the dimension of spectral information to extract the characteristic wavelength. Finally, an integrated model with three weak classifications was used for soybean oil detection by stacking integrated learning. The results showed that three obvious absorption peaks existed at 1747 cm⁻¹, 2858 cm⁻¹, and 2927 cm⁻¹ for soluble sugars and unsaturated oils, and the model based on stacking integrated learning could improve the classification accuracy from 0.9499 to 0.9846. The results prove that photoacoustic spectroscopy has a good detection ability for edible oil quality detection. Full article

(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis)

► Show Figures

Figure 1

19 pages, 4969 KiB

Open AccessArticle

Optimization Analysis of Thermodynamic Characteristics of Serrated Plate-Fin Heat Exchanger

by Ying Guan, Liquan Wang and Hongjiang Cui

Sensors 2023, 23(8), 4158; https://doi.org/10.3390/s23084158 - 21 Apr 2023

Cited by 2 | Viewed by 1445

Abstract

This study explores the use of Multi-Objective Genetic Algorithm (MOGA) for thermodynamic characteristics of serrated plate-fin heat exchanger (PFHE) under numerical simulation method. Numerical investigations on the important structural parameters of the serrated fin and the j factor and the f factor of [...] Read more.

This study explores the use of Multi-Objective Genetic Algorithm (MOGA) for thermodynamic characteristics of serrated plate-fin heat exchanger (PFHE) under numerical simulation method. Numerical investigations on the important structural parameters of the serrated fin and the j factor and the f factor of PFHE are conducted, and the experimental correlations about the j factor and the f factor are determined by comparing the simulation results with the experimental data. Meanwhile, based on the principle of minimum entropy generation, the thermodynamic analysis of the heat exchanger is investigated, and the optimization calculation is carried out by MOGA. The comparison results between optimized structure and original show that the j factor increases by 3.7%, the f factor decreases by 7.8%, and the entropy generation number decreases by 31%. From the data point of view, the optimized structure has the most obvious effect on the entropy generation number, which shows that the entropy generation number can be more sensitive to the irreversible changes caused by the structural parameters, and at the same time, the j factor is appropriately increased. Full article

(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis)

► Show Figures

Figure 1

13 pages, 3488 KiB

Open AccessCommunication

Error Analysis of Heterodyne Interferometry Based on One Single-Mode Polarization-Maintaining Fiber

by Yibin Qian, Jiakun Li, Qibo Feng, Qixin He and Fei Long

Sensors 2023, 23(8), 4108; https://doi.org/10.3390/s23084108 - 19 Apr 2023

Cited by 3 | Viewed by 1147

Abstract

Using polarization-maintaining fiber (PMF) in dual-frequency heterodyne interferometry has the advantages of reducing the laser’s own drift, obtaining high-quality light spots, and improving thermal stability. Using only one single-mode PMF to achieve the transmission of dual-frequency orthogonal, linearly polarized beam requires angular alignment [...] Read more.

Using polarization-maintaining fiber (PMF) in dual-frequency heterodyne interferometry has the advantages of reducing the laser’s own drift, obtaining high-quality light spots, and improving thermal stability. Using only one single-mode PMF to achieve the transmission of dual-frequency orthogonal, linearly polarized beam requires angular alignment only once to realize the transmission of dual-frequency orthogonal, linearly polarized light, avoiding coupling inconsistency errors, so that it has the advantages of high efficiency and low cost. However, there are still many nonlinear influencing factors in this method, such as the ellipticity and non-orthogonality of the dual-frequency laser, the angular misalignment error of the PMF, and the influence of temperature on the output beam of the PMF. This paper uses the Jones matrix to innovatively construct an error analysis model for the heterodyne interferometry using one single-mode PMF, to realize the quantitative analysis of various nonlinear error influencing factors, and clarify that the main error source is the angular misalignment error of the PMF. For the first time, the simulation provides a goal for the optimization of the alignment scheme of the PMF and the improvement of the accuracy to the sub-nanometer level. In actual measurement, the angular misalignment error of the PMF needs to be smaller than 2.87° to achieve sub-nanometer interference accuracy, and smaller than 0.25° to make the influence smaller than ten picometers. It provides theoretical guidance and an effective means for improving the design of heterodyne interferometry instruments based on PMF and further reducing measurement errors. Full article

(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis)

► Show Figures

Figure 1

18 pages, 9576 KiB

Open AccessArticle

Fault Diagnosis for Abnormal Wear of Rolling Element Bearing Fusing Oil Debris Monitoring

by Yulai Zhao, Xiaowei Wang, Shuo Han, Junzhe Lin and Qingkai Han

Sensors 2023, 23(7), 3402; https://doi.org/10.3390/s23073402 - 23 Mar 2023

Cited by 8 | Viewed by 1661

Abstract

The abnormal wear of a rolling element bearing caused by early failures, such as pitting and spalling, will deteriorate the running state and reduce the life. This paper demonstrates the importance of oil debris monitoring and its effective feature extraction for bearing health [...] Read more.

The abnormal wear of a rolling element bearing caused by early failures, such as pitting and spalling, will deteriorate the running state and reduce the life. This paper demonstrates the importance of oil debris monitoring and its effective feature extraction for bearing health assessment. In this paper, a rolling bearing-rotor test rig with forced lubrication is set up and the nonferrous contaminants with higher hardness were introduced artificially to accelerate the occurrence of pitting and spalling. The early failure and abnormal wear of rolling bearings cannot be effectively detected only through the vibration signal; the temperature and oil debris monitoring data are also collected synchronously. Two features regarding the ferrous particle size distribution are extracted and fused with vibration based-features to form a feature set. The sensitive features are extracted from the features set using the Neighborhood Component Analysis method to avoid feature redundancy. Finally, the importance of the oil debris based-features for the diagnosis of abnormal bearing wear is analyzed with different machine learning algorithms. Taking SVM classifier as an example, the experiment results show that the introduction of oil debris based-features increases the diagnostic accuracy by 15.7%. Full article

(This article belongs to the Special Issue Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Multi-Sensor Information Fusion, Advanced Signal Analysis, and Intelligent Fault Diagnosis

Share This Special Issue

Special Issue Editors

Special Issue Information

Related Special Issue

Published Papers (12 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI