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Intelligent Sensing and Monitoring for Industrial Process

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 29680

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Special Issue Editors

Prof. Dr. Chunhua Yang

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Guest Editor

School of Automation, Central South University, Changsha 410083, China
Interests: intelligent sensing and automatic device; process modeling and optimal control; industrial big data analysis and deep learning; smart manufacturing for process industry
Special Issues, Collections and Topics in MDPI journals

Dr. Qiwu Luo

E-Mail Website
Guest Editor

School of Automation, Central South University, Changsha 410083, China
Interests: computer vision; molecular spectroscopy; industrial automatic optic inspection; parallel hardware architecture design; reconfigurable computing

Special Issue Information

Dear Colleagues,

Intelligent sensing and monitoring plug intelligent wings into the traditional industries. They provide key data feedback for production quality monitoring and operational controls of the industrial process, which is crucial for efficient control, energy saving and emission reduction of the production process. However, industrial detection faces harsh environments and complex processes, which leads to difficulties in obtaining and analyzing vital information in the industrial process. The intelligent sensing and smart algorithms have potential advantages in breaking through the limitations of conventional detection methods, overcoming the challenges of strong interference in the complicated industrial processes, accurately extracting key features, and intelligently analyzing industrial information.

In this present Special Issue, we invite papers on intelligent sensing and monitoring for industrial application scenarios. The purposes of the Special Issue are to collect contributions in the methodologies and systems of spectral analysis, laser-based measurement, visual-based inspection, and so on, to serve as a forum for researchers in the field of sensing technologies and signal processing strategies, and to promote the developments of automatic inspection of product quality and intelligent control of industrial processes. Theoretical and experimental works are both welcome, aimed at providing a series of state-of-the-art approaches to extract informative features, to realize smart signal processing from weak and noisy raw data, and to move towards the progression of noise-robust and environment-adaptive monitoring techniques. Critical reviews and surveys of the cutting-edge and practice are also encouraged.

Prof. Dr. Chunhua Yang
Dr. Qiwu Luo
Guest Editors

If you want to learn more information or need any advice, you can contact the Special Issue Editor Jessica Zhou via <[email protected]> directly.

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Keywords

TDLAS-based measurement
laser-based measurement
spectral analysis
trace gas detection
automated visual inspection
in-situ defect detection
combustion diagnosis
particle measurement
portable instrument
holography
interferometry

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Published Papers (9 papers)

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Research

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32 pages, 134307 KiB

Open AccessArticle

Moisture Determination for Fine-Sized Copper Ore by Computer Vision and Thermovision Methods

by Dariusz Buchczik, Sebastian Budzan, Oliwia Krauze and Roman Wyzgolik

Sensors 2023, 23(3), 1220; https://doi.org/10.3390/s23031220 - 20 Jan 2023

Cited by 2 | Viewed by 2580

Abstract

The moisture of bulk material has a significant impact on the energetic efficiency of dry grinding, resultant particle size distribution and particle shape, and conditions of powder transport. This research aims to develop computer vision and thermovision techniques for the on-site estimation of moisture content in copper ore, for use, e.g., in dry grinding installations. The influence of particle size on the results of moisture estimation is also studied. The tested granular material was copper ore of particle size 0–2 mm and relative moisture content of 0.5–11%. Both vision and thermovision images were taken at standard and macro scales. The results suggest that median-intensity vision images monotonically reflect copper ore moisture in the range of about 0.5–5%. Suitable models were identified and cross-validated here. In contrary, thermograms should not be analyzed simply for their mean temperature but treated with computer vision processing algorithms. Full article

(This article belongs to the Special Issue Intelligent Sensing and Monitoring for Industrial Process)

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15 pages, 25609 KiB

Open AccessArticle

Tire Speckle Interference Bubble Defect Detection Based on Improved Faster RCNN-FPN

by Shihao Yang, Dongmei Jiao, Tongkun Wang and Yan He

Sensors 2022, 22(10), 3907; https://doi.org/10.3390/s22103907 - 21 May 2022

Cited by 13 | Viewed by 2668

Abstract

With the development of neural networks, object detection based on deep learning is developing rapidly, and its applications are gradually increasing. In the tire industry, detecting speckle interference bubble defects of tire crown has difficulties such as low image contrast, small object scale, and large internal differences of defects, which affect the detection precision. To solve these problems, we propose a new feature pyramid network based on Faster RCNN-FPN. It can fuse features across levels and directions to improve small object detection and localization, and increase object detection precision. The method has proven its effectiveness through cross-validation experiments. On a tire crown bubble defect dataset, the mAP [0.5:0.95] increased by 2.08% and the AP0.5 increased by 2.4% over the original network. The results show that the improved network significantly improves detecting tire crown bubble defects. Full article

(This article belongs to the Special Issue Intelligent Sensing and Monitoring for Industrial Process)

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15 pages, 1596 KiB

Open AccessArticle

Semi-Supervised Learning for Defect Segmentation with Autoencoder Auxiliary Module

by Bee-ing Sae-ang, Wuttipong Kumwilaisak and Pakorn Kaewtrakulpong

Sensors 2022, 22(8), 2915; https://doi.org/10.3390/s22082915 - 11 Apr 2022

Cited by 6 | Viewed by 2741

Abstract

In general, one may have access to a handful of labeled normal and defect datasets. Most unlabeled datasets contain normal samples because the defect samples occurred rarely. Thus, the majority of approaches for anomaly detection are formed as unsupervised problems. Most of the previous methods have typically chosen an autoencoder to extract the common characteristics of the unlabeled dataset, assumed as normal characteristics, and determine the unsuccessfully reconstructed area as the defect area in an image. However, we could waste the ground truth data if we leave them unused. In addition, a suitable choice of threshold value is needed for anomaly segmentation. In our study, we propose a semi-supervised setting to make use of both unlabeled and labeled samples and the network is trained to segment out defect regions automatically. We first train an autoencoder network to reconstruct defect-free images from an unlabeled dataset, mostly containing normal samples. Then, a difference map between the input and the reconstructed image is calculated and feeds along with the corresponding input image into the subsequent segmentation module. We share the ground truth for both kinds of input and train the network with binary cross-entropy loss. Additional difference images can also increase stability during training. Finally, we show extensive experimental results to prove that, with help from a handful of ground-truth segmentation maps, the result is improved overall by 3.83%. Full article

(This article belongs to the Special Issue Intelligent Sensing and Monitoring for Industrial Process)

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17 pages, 2905 KiB

Open AccessArticle

Smoothing Complete Feature Pyramid Networks for Roll Mark Detection of Steel Strips

by Qiwu Luo, Weiqiang Jiang, Jiaojiao Su, Jiaqiu Ai and Chunhua Yang

Sensors 2021, 21(21), 7264; https://doi.org/10.3390/s21217264 - 31 Oct 2021

Cited by 9 | Viewed by 2718

Abstract

Steel strip acts as a fundamental material for the steel industry. Surface defects threaten the steel quality and cause substantial economic and reputation losses. Roll marks, always occurring periodically in a large area, are put on the top of the list of the most serious defects by steel mills. Essentially, the online roll mark detection is a tiny target inspection task in high-resolution images captured under harsh environment. In this paper, a novel method—namely, Smoothing Complete Feature Pyramid Networks (SCFPN)—is proposed for the above focused task. In particular, the concept of complete intersection over union (CIoU) is applied in feature pyramid networks to obtain faster fitting speed and higher prediction accuracy by suppressing the vanishing gradient in training process. Furthermore, label smoothing is employed to promote the generalization ability of model. In view of lack of public surface image database of steel strips, a raw defect database of hot-rolled steel strip surface, CSU_STEEL, is opened for the first time. Experiments on two public databases (DeepPCB and NEU) and one fresh texture database (CSU_STEEL) indicate that our SCFPN yields more competitive results than several prestigious networks—including Faster R-CNN, SSD, YOLOv3, YOLOv4, FPN, DIN, DDN, and CFPN. Full article

(This article belongs to the Special Issue Intelligent Sensing and Monitoring for Industrial Process)

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16 pages, 743 KiB

Open AccessArticle

Binary Neural Network for Automated Visual Surface Defect Detection

by Wenzhe Liu, Jiehua Zhang, Zhuo Su, Zhongzhu Zhou and Li Liu

Sensors 2021, 21(20), 6868; https://doi.org/10.3390/s21206868 - 16 Oct 2021

Cited by 5 | Viewed by 2694

Abstract

As is well-known, defects precisely affect the lives and functions of the machines in which they occur, and even cause potentially catastrophic casualties. Therefore, quality assessment before mounting is an indispensable requirement for factories. Apart from the recognition accuracy, current networks suffer from excessive computing complexity, making it of great difficulty to deploy in the manufacturing process. To address these issues, this paper introduces binary networks into the area of surface defect detection for the first time, for the reason that binary networks prohibitively constrain weight and activation to +1 and −1. The proposed Bi-ShuffleNet and U-BiNet utilize binary convolution layers and activations in low bitwidth, in order to reach comparable performances while incurring much less computational cost. Extensive experiments are conducted on real-life NEU and Magnetic Tile datasets, revealing the least OPs required and little accuracy decline. When classifying the defects, Bi-ShuffleNet yields comparable results to counterpart networks, with at least 2× inference complexity reduction. Defect segmentation results indicate similar observations. Some network design rules in defect detection and binary networks are also summarized in this paper. Full article

(This article belongs to the Special Issue Intelligent Sensing and Monitoring for Industrial Process)

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17 pages, 6346 KiB

Open AccessArticle

Fault Detection of Wind Turbine Electric Pitch System Based on IGWO-ERF

by Mingzhu Tang, Jiabiao Yi, Huawei Wu and Zimin Wang

Sensors 2021, 21(18), 6215; https://doi.org/10.3390/s21186215 - 16 Sep 2021

Cited by 13 | Viewed by 2414

Abstract

It is difficult to optimize the fault model parameters when Extreme Random Forest is used to detect the electric pitch system fault model of the double-fed wind turbine generator set. Therefore, Extreme Random Forest which was optimized by improved grey wolf algorithm (IGWO-ERF) was proposed to solve the problems mentioned above. First, IGWO-ERF imports the Cosine model to nonlinearize the linearly changing convergence factor α to balance the global exploration and local exploitation capabilities of the algorithm. Then, in the later stage of the algorithm iteration, α wolf generates its mirror wolf based on the lens imaging learning strategy to increase the diversity of the population and prevent local optimum of the population. The electric pitch system fault detection method of the wind turbine generator set sets the generator power of the variable pitch system as the main state parameter. First, it uses the Pearson correlation coefficient method to eliminate the features with low correlation with the electric pitch system generator power. Then, the remaining features are ranked by the importance of the RF features. Finally, the top N features are selected to construct the electric pitch system fault data set. The data set is divided into a training set and a test set. The training set is used to train the proposed fault detection model, and the test set is used for testing. Compared with other parameter optimization algorithms, the proposed method has lower FNR and FPR in the electric pitch system fault detection of the wind turbine generator set. Full article

(This article belongs to the Special Issue Intelligent Sensing and Monitoring for Industrial Process)

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17 pages, 13263 KiB

Open AccessArticle

Steel Wire Rope Surface Defect Detection Based on Segmentation Template and Spatiotemporal Gray Sample Set

by Guoyong Zhang, Zhaohui Tang, Ying Fan, Jinping Liu, Hadi Jahanshahi and Ayman A. Aly

Sensors 2021, 21(16), 5401; https://doi.org/10.3390/s21165401 - 10 Aug 2021

Cited by 12 | Viewed by 3781

Abstract

Machine-vision-based defect detection, instead of manual visual inspection, is becoming increasingly popular. In practice, images of the upper surface of cableway load sealing steel wire ropes are seriously affected by complex environments, including factors such as lubricants, adhering dust, natural light, reflections from metal or oil stains, and lack of defect samples. This makes it difficult to directly use traditional threshold-segmentation-based or supervised machine-learning-based defect detection methods for wire rope strand segmentation and fracture defect detection. In this study, we proposed a segmentation-template-based rope strand segmentation method with high detection accuracy, insensitivity to light, and insensitivity to oil stain interference. The method used the structural characteristics of steel wire rope to create a steel wire rope segmentation template, the best coincidence position of the steel wire rope segmentation template on the real-time edge image was obtained through multiple translations, and the steel wire rope strands were segmented. Aiming at the problem of steel wire rope fracture defect detection, inspired by the idea of dynamic background modeling, a steel wire rope surface defect detection method based on a steel wire rope segmentation template and a timely spatial gray sample set was proposed. The spatiotemporal gray sample set of each pixel in the image was designed by using the gray similarity of the same position in the time domain and the gray similarity of pixel neighborhood in the space domain, the dynamic gray background of wire rope surface image was constructed to realize the detection of wire rope surface defects. The method proposed in this paper was tested on the image set of Z-type double-layer load sealing steel wire rope of mine ropeway, and compared with the classic dynamic background modeling methods such as VIBE, KNN, and MOG2. The results show that the purposed method is more accurate, more effective, and has strong adaptability to complex environments. Full article

(This article belongs to the Special Issue Intelligent Sensing and Monitoring for Industrial Process)

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21 pages, 4663 KiB

Open AccessArticle

A Synchronous Prediction Model Based on Multi-Channel CNN with Moving Window for Coal and Electricity Consumption in Cement Calcination Process

by Xin Shi, Gaolu Huang, Xiaochen Hao, Yue Yang and Ze Li

Sensors 2021, 21(13), 4284; https://doi.org/10.3390/s21134284 - 23 Jun 2021

Cited by 19 | Viewed by 2718

Abstract

The precision and reliability of the synchronous prediction of multi energy consumption indicators such as electricity and coal consumption are important for the production optimization of industrial processes (e.g., in the cement industry) due to the deficiency of the coupling relationship of the two indicators while forecasting separately. However, the time lags, coupling, and uncertainties of production variables lead to the difficulty of multi-indicator synchronous prediction. In this paper, a data driven forecast approach combining moving window and multi-channel convolutional neural networks (MWMC-CNN) was proposed to predict electricity and coal consumption synchronously, in which the moving window was designed to extract the time-varying delay feature of the time series data to overcome its impact on energy consumption prediction, and the multi-channel structure was designed to reduce the impact of the redundant parameters between weakly correlated variables of energy prediction. The experimental results implemented by the actual raw data of the cement plant demonstrate that the proposed MWMC-CNN structure has a better performance than without the combination structure of the moving window multi-channel with convolutional neural network. Full article

(This article belongs to the Special Issue Intelligent Sensing and Monitoring for Industrial Process)

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Review

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28 pages, 2535 KiB

Open AccessReview

Three-Dimensional Morphology and Size Measurement of High-Temperature Metal Components Based on Machine Vision Technology: A Review

by Xin Wen, Jingpeng Wang, Guangyu Zhang and Lianqiang Niu

Sensors 2021, 21(14), 4680; https://doi.org/10.3390/s21144680 - 8 Jul 2021

Cited by 10 | Viewed by 5044

Abstract

The three-dimensional (3D) size and morphology of high-temperature metal components need to be measured in real time during manufacturing processes, such as forging and rolling. Since the surface temperature of a metal component is very high during the forming and manufacturing process, manually measuring the size of a metal component at a close distance is difficult; hence, a non-contact measurement technology is required to complete the measurement. Recently, machine vision technology has been developed, which is a non-contact measurement technology that only needs to capture multiple images of a measured object to obtain the 3D size and morphology information, and this technology can be used in some extreme conditions. Machine vision technology has been widely used in industrial, agricultural, military and other fields, especially fields involving various high-temperature metal components. This paper provides a comprehensive review of the application of machine vision technology in measuring the 3D size and morphology of high-temperature metal components. Furthermore, according to the principle and method of measuring equipment structures, this review highlights two aspects in detail: laser scanning measurement and multi-view stereo vision technology. Special attention is paid to each method through comparisons and analyses to provide essential technical references for subsequent researchers. Full article

(This article belongs to the Special Issue Intelligent Sensing and Monitoring for Industrial Process)

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