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Advances in Machine Condition Monitoring and Fault Diagnosis

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 35680

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Special Issue Editors

Dr. Wenxian Yang

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Guest Editor

School of Engineering, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
Interests: marine and offshore renewable energy; condition monitoring; fault diagnosis; maintenance; reliability; signal processing; computational fluid dynamics; hydrodynamics; wind farm management; offshore engineering; electric vehicles
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Radoslaw Zimroz

E-Mail Website
Guest Editor

Wrocław University of Science and Technology, Faculty of Mining, Wroclaw, Poland
Interests: technical diagnostics; condition monitoring gearboxes; bearings; mining machines signal processing; data analysis time varying systems pattern recognition

Dr. Mayorkinos Papaelias

E-Mail Website
Guest Editor

School of Metallurgy and Materials, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Interests: non-destructive evaluation; structural health monitoring; condition monitoring; machine learning; digital twins
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Condition monitoring and fault diagnosis have demonstrated its effectiveness in improving the operation, maintenance, availability and therefore economic return of machines. They will play a more vital role in the future industrial production process with the rapid development of modern machinery industry. That requests the condition monitoring and fault diagnosis techniques to be more reliable and efficient in practical use. However, this is challenged by the fact that modern machines are becoming more complex in structure, larger in size, and operate under harsher loading and operational conditions. This Special Issue will provide an open platform for reporting and sharing the latest advances in this field. The topics of interest for publication in this Special issue include, but are not limited to, the following:

Machine condition monitoring
Structural health condition monitoring
Signal processing
Data analysis
Image processing
Fault diagnosis
Non-destructive testing
Non-destructive evaluation
Mathematical models for health monitoring
Data mining
Artificial intelligence

Dr. Wenxian Yang
Prof. Dr. Radoslaw Zimroz
Dr. Mayorkinos Papaelias
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Condition monitoring
Fault diagnosis
Signal processing
Data analysis
Non-destructive testing
Non-destructive evaluation

Published Papers (12 papers)

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Editorial

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5 pages, 176 KiB

Open AccessEditorial

Advances in Machine Condition Monitoring and Fault Diagnosis

by Wenxian Yang, Radoslaw Zimroz and Mayorkinos Papaelias

Electronics 2022, 11(10), 1563; https://doi.org/10.3390/electronics11101563 - 13 May 2022

Cited by 7 | Viewed by 1685

Abstract

In the past few decades, with the great progress made in the field of computer technology, non-destructive testing, signal and image processing, and artificial intelligence, machine condition monitoring and fault diagnosis technology have also achieved great technological progress and played an active and important role in various industries to ensure the efficient and reliable operation of machines, lower the operation and maintenance costs, and improve the reliability and availability of large critical equipment [...] Full article

(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)

Research

Jump to: Editorial

16 pages, 4197 KiB

Open AccessArticle

Machine Learning-Based Structural Health Monitoring Using RFID for Harsh Environmental Conditions

by Aobo Zhao, Ali Imam Sunny, Li Li and Tengjiao Wang

Electronics 2022, 11(11), 1740; https://doi.org/10.3390/electronics11111740 - 30 May 2022

Cited by 7 | Viewed by 2423

Abstract

Post Operation Clean Out (POCO) is the process to remove hazardous materials and decommission nuclear facilities at the end of a nuclear plant’s lifetime. The introduction of Internet of Things (IoT) technologies in the environment, especially radio frequency identification (RFID), would improve efficiency and safety by intelligently monitoring POCO activities. In this paper, we present a passive material identification and crack sensing method developed for the integration of sensing and communication using commercial off-the-shelf (COTS) RFID tags, which is a long-term solution to material property monitoring under insulation for harsh environmental conditions. To validate the effectiveness of material identification and crack monitoring, machine learning techniques have been applied, and the feasibility of the study has been outlined. The result shows that the material identification can be achieved with traditional features and obtain improved accuracy with three-layer multi-layer neural networks (MLNN). In crack characterization, the tree algorithm based on traditional features achieves a reasonable accuracy, while three-layer MLNN is the best solution, which supports the efficiency of traditional feature extraction methods in specific applications. Full article

(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)

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14 pages, 6993 KiB

Open AccessArticle

Predictive Maintenance System for Wafer Transport Robot Using K-Means Algorithm and Neural Network Model

by Ji-Hyun Yoo, Young-Kook Park and Seung-Soo Han

Electronics 2022, 11(9), 1324; https://doi.org/10.3390/electronics11091324 - 21 Apr 2022

Cited by 8 | Viewed by 2404

Abstract

Maintenance is the technology of continuously monitoring the conditions of equipment and predicting the timing of maintenance for equipment. Particularly in the field of semiconductor manufacturing, where processes are automated, various methods are being tried to minimize the economic losses and maintenance costs caused by equipment failure. A new Predictive Maintenance (PdM) technique, a new method of maintenance, is introduced in this paper to develop an algorithm for predicting the failure of wafer transfer robots in advance. The acceleration sensor data used in the experiment were obtained by installing a sensor onto the wafer transfer robot. To analyze these data, the data preprocessing and FFT process were performed. These data were divided into normal data, first error data, second error data, and third error data (failure data) in stages. By clustering the data using the K-means algorithm, the center point distribution of the clusters was analyzed, and the features of the error data and normal data were extracted. Using these features, an artificial neural network model was designed to predict the point of failure of the robot. Previous research on maintenance systems of the transfer robot used fewer than 50 error data, but 1686 error data were used in this experiment. The reliability of the model is improved by randomly selecting data from a total of 2248 data sets. In addition, it was confirmed that it was possible to classify normal data and error data with an accuracy of 97% and to predict equipment failure by applying neural network modeling. Full article

(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)

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10 pages, 4093 KiB

Open AccessArticle

On-Wafer Temperature Monitoring Sensor for Condition Monitoring of Repaired Electrostatic Chuck

by Jae-Hwan Kim, Yoonsung Koo, Wansoo Song and Sang Jeen Hong

Electronics 2022, 11(6), 880; https://doi.org/10.3390/electronics11060880 - 10 Mar 2022

Cited by 8 | Viewed by 7560

Abstract

The temperature of electrostatic chuck (ESC), a wafer susceptor used in semiconductor etch equipment, must accurately control the temperature of wafers during the etching process to obtain uniform and consistent process results. Failure to control the precise temperature can lead to rejection from the high-volume semiconductor manufacturing site (one of the most high-cost equipment components which can be repaired for its extended use). In this research, we propose a wireless-type on-wafer temperature monitoring system (OTMS) for easier and faster temperature monitoring to help temperature measurements of the repaired ESC in atmospheric and vacuum conditions. The proposed method, which can effectively measure the temperature distribution of the ESC, should manage the operational condition of ESC. A successful demonstration of the 300 mm size OTMS for the repaired parts enhanced the quality assurance with a temperature deviation of ±3.83 °C over 65 points of measurement. Full article

(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)

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18 pages, 1683 KiB

Open AccessArticle

A Parallel Deep Reinforcement Learning Framework for Controlling Industrial Assembly Lines

by Andrea Tortorelli, Muhammad Imran, Francesco Delli Priscoli and Francesco Liberati

Electronics 2022, 11(4), 539; https://doi.org/10.3390/electronics11040539 - 11 Feb 2022

Cited by 7 | Viewed by 3134

Abstract

Decision-making in a complex, dynamic, interconnected, and data-intensive industrial environment can be improved with the assistance of machine-learning techniques. In this work, a complex instance of industrial assembly line control is formalized and a parallel deep reinforcement learning approach is presented. We consider an assembly line control problem in which a set of tasks (e.g., vehicle assembly tasks) needs to be planned and controlled during their execution, with the aim of optimizing given key performance criteria. Specifically, the aim will be that of planning the task in order to minimize the total time taken to execute all the tasks (also called cycle time). Tasks run on workstations in the assembly line. To run, tasks need specific resources. Therefore, the tackled problem is that of optimally mapping tasks and resources to workstations, and deciding the optimal execution times of the tasks. In doing so, several constraints need to be respected (e.g., precedence constraints among the tasks, constraints on needed resources to run tasks, deadlines, etc.). The proposed approach uses deep reinforcement learning to learn a tasks/resources mapping policy that is effective in minimizing the resulting cycle time. The proposed method allows us to explicitly take into account all the constraints, and, once training is complete, can be used in real time to dynamically control the execution of tasks. Another motivation for the proposed work is in the ability of the used method to also work in complex scenarios, and in the presence of uncertainties. As a matter of fact, the use of deep neural networks allows for learning the model of the assembly line problem, in contrast with, e.g., optimization-based techniques, which require explicitly writing all the equations of the model of the problem. In order to speed up the training phase, we adopt a learning scheme in which more agents are trained in parallel. Simulations show that the proposed method can provide effective real-time decision support to industrial operators for scheduling and rescheduling activities, achieving the goal of minimizing the total tasks’ execution time. Full article

(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)

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14 pages, 16521 KiB

Open AccessArticle

Use of Optical Emission Spectroscopy Data for Fault Detection of Mass Flow Controller in Plasma Etch Equipment

by Hyukjoon Kwon and Sang Jeen Hong

Electronics 2022, 11(2), 253; https://doi.org/10.3390/electronics11020253 - 13 Jan 2022

Cited by 10 | Viewed by 2453

Abstract

To minimize wafer yield losses by misprocessing during semiconductor manufacturing, faster and more accurate fault detection during the plasma process are desired to increase production yields. Process faults can be caused by abnormal equipment conditions, and the performance drifts of the parts or components of complicated semiconductor fabrication equipment are some of the most unnoticed factors that eventually change the plasma conditions. In this work, we propose improved stability and accuracy of process fault detection using optical emission spectroscopy (OES) data. Under a controlled experimental setup of arbitrarily induced fault scenarios, the extended isolation forest (EIF) approach was used to detect anomalies in OES data compared with the conventional isolation forest method in terms of accuracy and speed. We also used the OES data to generate features related to electron temperature and found that using the electron temperature features together with equipment status variable identification data (SVID) and OES data improved the prediction accuracy of process/equipment fault detection by a maximum of 0.84%. Full article

(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)

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16 pages, 5149 KiB

Open AccessArticle

A Flexible Turning and Sensing System for Pressure Ulcers Prevention

by Ying Zhang, Xiaofeng Zou, Bin Zhang, Yi Han, Shuoyu Wang, Tao Liu and Xiufeng Zhang

Electronics 2021, 10(23), 2971; https://doi.org/10.3390/electronics10232971 - 29 Nov 2021

Cited by 6 | Viewed by 3338

Abstract

Pressure ulcers (PU) are one of the most frequent hazards of long-term bedridden patients. With the continuous increase of aging, the number of long-term bedridden disabled and semi-disabled elderly people is increasing. At the same time, there is a serious shortage of professional pressure ulcer nursing staff. There is also a lack of flexible turning equipment for PU prevention. The research in the field of pressure ulcer prevention at home and abroad is carried out steadily, and the equipment for turning over by pneumatic or mechanical drive is developed. However, these devices often have insurmountable defects, such as complex structure, cost constraints, difficult control, weak body feeling, and so on. Under these circumstances, a set of pneumatic turnover mattresses based on clinical nursing methods have been developed. The mattress is divided into a turnover area and two support areas. The turnover airbag is linked with the support airbag to improve the patient’s comfort when passively turning over. The turnover amplitude and interval can be adjusted to provide a personalized turnover experience for bedridden patients. To improve the safety of the turning mattress during automatic turning, we also add a temperature sensor based on the principle of infrared reflection to monitor the status of bedridden patients, which can realize real-time temperature measurement, monitoring of getting out of bed and monitoring of the turning process. Full article

(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)

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23 pages, 9017 KiB

Open AccessArticle

A Three-Stage Data-Driven Approach for Determining Reaction Wheels’ Remaining Useful Life Using Long Short-Term Memory

by Md Sirajul Islam and Afshin Rahimi

Electronics 2021, 10(19), 2432; https://doi.org/10.3390/electronics10192432 - 07 Oct 2021

Cited by 7 | Viewed by 2220

Abstract

Reaction wheels are widely used in the attitude control system of small satellites. Unfortunately, reaction wheels failure restricts the efficacy of a satellite, and it is one of the many reasons leading to premature abandonment of the satellites. This study observes the measurable system parameter of a faulty reaction wheel induced with incipient fault to estimate the remaining useful life of the reaction wheels. We achieve this goal in three stages, as none of the observable system parameters are directly related to the health of a reaction wheel. In the first stage, we identify the necessary observable system parameter and predict the future of these parameters using sensor acquired data and a long short-term memory recurrent neural network. In the second stage, we estimate the health index parameter using a multivariate long short-term memory network. In the third stage, we predict the remaining useful life of reaction wheels based on historical data of the health index parameter. Normalized root mean squared error is used to evaluate the performance of the various models in each stage. Additionally, three different timespans (short, moderate, and extended in the scale of small satellite orbit times) are simulated and tested for the performance of the proposed methodology regarding the malfunction of reaction wheels. Furthermore, the robustness of the proposed method to missing values, input frequency, and noise is studied. The results show promising performance for the proposed scheme with accuracy in predicting health index parameter around 0.01–0.02 normalized root mean squared error, the accuracy in prediction of RUL of 1%–2.5%, and robustness to various uncertainty factors, as discussed above. Full article

(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)

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14 pages, 6030 KiB

Open AccessArticle

Thermal Defect Detection for Substation Equipment Based on Infrared Image Using Convolutional Neural Network

by Kaixuan Wang, Jiaqiao Zhang, Hongjun Ni and Fuji Ren

Electronics 2021, 10(16), 1986; https://doi.org/10.3390/electronics10161986 - 18 Aug 2021

Cited by 12 | Viewed by 2846

Abstract

Thermal defects of substation equipment have a great impact on the stability of power systems. Temperature is crucial for thermal defect detection in infrared images. The traditional detection methods, which have low efficiency and poor accuracy, record the temperature of infrared images manually. In this study, a thermal defect detection method based on infrared images using a convolutional neural network (CNN) is proposed. Firstly, the improved pre-processing method is applied to reduce background information, and the region of interest is located according to the contour and position information, hence improving the quality of images. Then, the temperature values are segmented to establish the dataset (T-IR11), which contains 11 labels. Finally, the CNN model is constructed to extract features, and the support vector machine is trained for classification. To verify the effectiveness of the proposed method, precision, recall, and F₁ score are adopted and 10-fold cross-validation is employed on the T-IR11 dataset. The results demonstrate that the accuracy of the proposed method is 99.50%, and the performance is superior to that of previous methods in terms of infrared images. The proposed method can realize automatic temperature recognition and equipment with thermal defects can be recorded systematically, which has significant practical value for defect detection in substation equipment. Full article

(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)

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Graphical abstract

15 pages, 4828 KiB

Open AccessArticle

Novel MOA Fault Detection Technology Based on Small Sample Infrared Image

by Baoquan Wei, Yong Zuo, Yande Liu, Wei Luo, Kaiyun Wen and Fangming Deng

Electronics 2021, 10(15), 1748; https://doi.org/10.3390/electronics10151748 - 21 Jul 2021

Cited by 8 | Viewed by 2021

Abstract

This paper proposes a novel metal oxide arrester (MOA) fault detection technology based on a small sample infrared image. The research is carried out from the detection process and data enhancement. A lightweight MOA identification and location algorithm is designed at the edge, which can not only reduce the amount of data uploaded, but also reduce the search space of cloud algorithm. In order to improve the accuracy and generalization ability of the defect detection model under the condition of small samples, a multi-model fusion detection algorithm is proposed. Different features of the image are extracted by multiple convolutional neural networks, and then multiple classifiers are trained. Finally, the weighted voting strategy is used for fault diagnosis. In addition, the extended model of fault samples is constructed by transfer learning and deep convolutional generative adversarial networks (DCGAN) to solve the problem of unbalanced training data sets. The experimental results show that the proposed method can realize the accurate location of arrester under the condition of small samples, and after the data expansion, the recognition rate of arrester anomalies can be improved from 83% to 85%, showing high effectiveness and reliability. Full article

(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)

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17 pages, 25877 KiB

Open AccessArticle

A Decision-Making Method for Machinery Abnormalities Based on Neural Network Prediction and Bayesian Hypothesis Testing

by Gaojun Liu, Shan Yang, Gaixia Wang, Fenglei Li and Dongdong You

Electronics 2021, 10(14), 1610; https://doi.org/10.3390/electronics10141610 - 06 Jul 2021

Cited by 3 | Viewed by 1711

Abstract

For anomaly identification of predicted data in machinery condition monitoring, traditional threshold methods have problems during residual testing. It is difficult to make decisions when the residuals are close to the threshold and fluctuate. This paper proposes a Bayesian dynamic thresholding method that combines Bayesian inference with neural network signal prediction. The method makes full use of historical prior data to build an anomaly identification and warning model applicable under single variable or multidimensional variables. A long short-term memory signal prediction model is established, and then a Bayesian hypothesis testing-based anomaly identification strategy is presented to quantify the probability of anomaly occurrence and issue early warnings for anomalies beyond a certain probability. The model was applied to open data sets of a pumping station and actual operating data of a nuclear power turbine. The results indicate that the model successfully predicts the failure probability and failure time. The effectiveness of the proposed method is verified. Full article

(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)

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19 pages, 1927 KiB

Open AccessArticle

Data-Driven Fault Diagnosis for Satellite Control Moment Gyro Assembly with Multiple In-Phase Faults

by Hossein Varvani Farahani and Afshin Rahimi

Electronics 2021, 10(13), 1537; https://doi.org/10.3390/electronics10131537 - 24 Jun 2021

Cited by 7 | Viewed by 2030

Abstract

A satellite can only complete its mission successfully when all its subsystems, including the attitude control subsystem, are in healthy condition and work properly. Control moment gyroscope is a type of actuator used in the attitude control subsystems of satellites. Any fault in the control moment gyroscope can cause the satellite mission failure if it is not detected, isolated, and resolved in time. Fault diagnosis provides an opportunity to detect and isolate the occurring faults and, if accompanied by proactive remedial actions, it can avoid failure and improve the satellite reliability. In this paper, an enhanced data-driven fault diagnosis is introduced for fault isolation of multiple in-phase faults of satellite control moment gyroscopes that has not been addressed in the literature before with high accuracy. The proposed method is based on an optimized support vector machine, and the results yield fault predictions with up to 95.6% accuracy. In addition, a sensitivity analysis with regard to noise, missing values, and missing sensors is done. The results show that the proposed model is robust enough to be used in real applications. Full article

(This article belongs to the Special Issue Advances in Machine Condition Monitoring and Fault Diagnosis)

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