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Applied Sciences
Special Issues
Machine Condition Monitoring and Fault Diagnosis: From Theory to Application
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Machine Condition Monitoring and Fault Diagnosis: From Theory to Application

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 8780

Special Issue Editor

Dr. Feiyun Cong

E-Mail Website
Guest Editor
School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China
Interests: fault diagnosis method; fault modeling; condition monitoring
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern machines are becoming more complex in structure and operate under harsher loading and operational conditions. To ensure the efficient and reliable operation of machines, minimize unscheduled downtime, and lower operation and maintenance costs, it is necessary to develop intelligent fault diagnostic methods and assess their health state for the aim of identifying the mode, type, severity, and degradation trend of faults.

This Special Issue encourages and welcomes original research articles on machine fault detection, diagnosis, and prognosis. Potential topics include but are not limited to the following:

  • Fault diagnosis method based on various sensor data;
  • Signal processing;
  • Fault model research with changeable variable transfer path;
  • Fault diagnostics under non-stationary operating conditions;
  • Fault prediction;
  • Machine-learning-based fault diagnostics and condition monitoring;
  • Fatigue analysis of machinery.

Dr. Feiyun Cong
Guest Editor

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. Applied Sciences 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.

Published Papers (7 papers)

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Editorial

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2 pages, 146 KiB  
Editorial
Special Issue on Machine Condition Monitoring and Fault Diagnosis: From Theory to Application
by Feiyun Cong
Appl. Sci. 2023, 13(20), 11550; https://doi.org/10.3390/app132011550 - 22 Oct 2023
Cited by 1 | Viewed by 741
Abstract
Modern machines are becoming more complex in structure and are operating under harsher loading and operational conditions [...] Full article
(This article belongs to the Special Issue Machine Condition Monitoring and Fault Diagnosis: From Theory to Application)

Research

Jump to: Editorial

22 pages, 6471 KiB  
Article
System-Level Fault Diagnosis for an Industrial Wafer Transfer Robot with Multi-Component Failure Modes
by Inu Lee, Hyung Jun Park, Jae-Won Jang, Chang-Woo Kim and Joo-Ho Choi
Appl. Sci. 2023, 13(18), 10243; https://doi.org/10.3390/app131810243 - 12 Sep 2023
Cited by 3 | Viewed by 1229
Abstract
In the manufacturing industry, robots are constantly operated at high speed, which degrades their performance by the degradation of internal components, eventually reaching failure. To address this issue, a framework for system-level fault diagnosis is proposed, which consists of extracting useful features from [...] Read more.
In the manufacturing industry, robots are constantly operated at high speed, which degrades their performance by the degradation of internal components, eventually reaching failure. To address this issue, a framework for system-level fault diagnosis is proposed, which consists of extracting useful features from the motor control signal acquired during the operation, diagnosing the current health of each component using the features, and estimating the associated degradation in the robot system’s performance. Finally, a maintenance strategy is determined by evaluating how well the system performance is restored by the replacement of each component. The framework is demonstrated using the example of a wafer transfer robot in the semiconductor industry, in which the robot is operated under faults with various severities for two critical components: the harmonic drive and the timing belt. Features are extracted for the motor signal using wavelet packet decomposition, followed by feature selection by considering the trendability and separability of the fault severity. An artificial neural network model and Gaussian process regression are employed for the diagnosis of the components’ health and the system’s performance, respectively. Full article
(This article belongs to the Special Issue Machine Condition Monitoring and Fault Diagnosis: From Theory to Application)
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20 pages, 7065 KiB  
Article
Classification of Speed Sensor Faults Based on Shallow Neural Networks
by Kamila Jankowska, Mateusz Dybkowski, Viktor Petro and Karol Kyslan
Appl. Sci. 2023, 13(12), 7263; https://doi.org/10.3390/app13127263 - 18 Jun 2023
Cited by 2 | Viewed by 1141
Abstract
This paper presents a novel speed sensor fault detection, classification, and compensation mechanism in a permanent magnet synchronous motor (PMSM) drive system. Application is based on state variable observers and shallow neural networks (NN). Classical fault detection mechanism based on state variable observers [...] Read more.
This paper presents a novel speed sensor fault detection, classification, and compensation mechanism in a permanent magnet synchronous motor (PMSM) drive system. Application is based on state variable observers and shallow neural networks (NN). Classical fault detection mechanism based on state variable observers has been extended with neural networks. This enables improved detection efficiency and increases immunity to false alarms. In addition, the use of neural networks allowed for the classification of the failure type. Three types of failures are considered in the paper: signal loss, scaling error, and signal interference. The detection efficiency of the proposed solution is about 97%. On the other hand, the classification of the worst type of failure—signal loss—was achieved at the level of 100%. Other considered failure types are classified at the level of 80–90%. In addition, tests were carried out for two types of observers—model reference adaptive system and sliding mode observer—to compare the results. The work presents experimental results carried out for various operating conditions of the drive system. The failure classification times in the experimental tests were achieved at a level of less than 30 ms. Full article
(This article belongs to the Special Issue Machine Condition Monitoring and Fault Diagnosis: From Theory to Application)
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17 pages, 6187 KiB  
Article
Fault Prediction of Mechanical Equipment Based on Hilbert–Full-Vector Spectrum and TCDAN
by Lei Chen, Lijun Wei, Wenlong Li, Junhui Wang and Dongyang Han
Appl. Sci. 2023, 13(8), 4655; https://doi.org/10.3390/app13084655 - 07 Apr 2023
Cited by 1 | Viewed by 1245
Abstract
To solve the problem of “under-maintenance” and “over-maintenance” in the daily maintenance of equipment, the predictive maintenance method based on the running state of equipment has shown great advantages, and fault prediction is an important part of predictive maintenance. First, the spectrum information [...] Read more.
To solve the problem of “under-maintenance” and “over-maintenance” in the daily maintenance of equipment, the predictive maintenance method based on the running state of equipment has shown great advantages, and fault prediction is an important part of predictive maintenance. First, the spectrum information of the equipment is extracted by the Hilbert–full-vector spectrum as the input of fault prediction. Compared with the traditional spectrum, this spectrum information fuses the signals of two sensors in the same section of the device, which can reflect the actual operational state of the device more comprehensively. Then, the temporal convolutional network is used to predict the amplitudes of different feature frequencies, and the double-layer attention mechanism is introduced to mine the correlation between the corresponding amplitudes of different feature frequencies and between the data at different historical moments, to highlight the more important influencing factors. In this way, the prediction accuracy of the model for the amplitude corresponding to the feature frequency of concern is improved. Finally, experimental verification is carried out on the XJTU-SY dataset. The results show that the TCDAN model proposed in this paper is significantly superior to TCN, GRU, BiLSTM, and LSTM, which can provide a more effective decision-making basis for the predictive maintenance of equipment. Full article
(This article belongs to the Special Issue Machine Condition Monitoring and Fault Diagnosis: From Theory to Application)
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17 pages, 6569 KiB  
Article
Motor On-Line Fault Diagnosis Method Research Based on 1D-CNN and Multi-Sensor Information
by Yufeng Gu, Yongji Zhang, Mingrui Yang and Chengshan Li
Appl. Sci. 2023, 13(7), 4192; https://doi.org/10.3390/app13074192 - 25 Mar 2023
Cited by 4 | Viewed by 1535
Abstract
The motor is the primary impetus source of most mechanical equipment, and its failure will cause substantial economic losses and safety problems. Therefore, it is necessary to study online fault diagnosis techniques for motors, given the problems caused by shallow learning models or [...] Read more.
The motor is the primary impetus source of most mechanical equipment, and its failure will cause substantial economic losses and safety problems. Therefore, it is necessary to study online fault diagnosis techniques for motors, given the problems caused by shallow learning models or single-sensor fault analysis in previous motor fault diagnosis techniques, such as blurred fault features, inaccurate identification, and time and manpower consumption. In this paper, we proposed a model for motor fault diagnosis based on deep learning and multi-sensor information fusion. Firstly, a correlation adaptive weighting method is proposed in this paper, and it is used to integrate the collected multi-source homogeneous sensor information into multi-source heterogeneous sensor information through the data layer fusion. Secondly, the 1D-CNN is used to carry out feature extraction, feature layer fusion, and fault classification of multi-source heterogeneous information of the motor. Finally, the data of seven states (one healthy and six faulty) of the motor are collected by the motor drive test bench to realize the model’s training, testing, and verification. The experimental results show that the fault diagnosis accuracy of the model is 99.3%. Thus, this method has important practical implications for improving the accuracy of motor fault diagnosis further. Full article
(This article belongs to the Special Issue Machine Condition Monitoring and Fault Diagnosis: From Theory to Application)
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18 pages, 2818 KiB  
Article
Simultaneous Fault Diagnosis Based on Hierarchical Multi-Label Classification and Sparse Bayesian Extreme Learning Machine
by Qing Ye and Changhua Liu
Appl. Sci. 2023, 13(4), 2376; https://doi.org/10.3390/app13042376 - 13 Feb 2023
Cited by 2 | Viewed by 1084
Abstract
This paper proposes an intelligent simultaneous fault diagnosis model based on a hierarchical multi-label classification strategy and sparse Bayesian extreme learning machine. The intelligent diagnosis model compares the similarity between an unknown sample to be diagnosed and each single fault mode, then outputs [...] Read more.
This paper proposes an intelligent simultaneous fault diagnosis model based on a hierarchical multi-label classification strategy and sparse Bayesian extreme learning machine. The intelligent diagnosis model compares the similarity between an unknown sample to be diagnosed and each single fault mode, then outputs the probability of each fault mode occurring. First, multiple two-class sub-classifiers based on SBELM are trained by using single-fault samples to extract the correlation between various pairs of single-fault, and the sub-classifiers are integrated with the proposed hierarchical multi-label classification (HMLC) strategy to form the diagnostic model based on HMLC-SBELM. Then, samples of single faults and simultaneous faults are used to generate the optimal discriminative thresholds by using optimization algorithms. Finally, the probabilistic output generated by the HMLC-SBELM-based model is transformed into the final fault modes by using the optimal discriminative threshold. The model performance is evaluated by using actual vibration signals of the main reducer and is compared with several classical models. The contrastive results indicate that the proposed model is more accurate, efficient, and stable. Full article
(This article belongs to the Special Issue Machine Condition Monitoring and Fault Diagnosis: From Theory to Application)
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18 pages, 6310 KiB  
Article
Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT
by Qing Chen, Sheng Zheng, Xing Wu and Tao Liu
Appl. Sci. 2022, 12(21), 10807; https://doi.org/10.3390/app122110807 - 25 Oct 2022
Cited by 1 | Viewed by 1001
Abstract
In industry, early fault signals of rolling bearings are submerged in strong background noise, causing a low signal-to-noise ratio (SNR) and difficult diagnosis. This paper proposes a fault feature extraction method based on an optimized Laplacian wavelet dictionary (LWD) and the feature symbol [...] Read more.
In industry, early fault signals of rolling bearings are submerged in strong background noise, causing a low signal-to-noise ratio (SNR) and difficult diagnosis. This paper proposes a fault feature extraction method based on an optimized Laplacian wavelet dictionary (LWD) and the feature symbol search (FSS) algorithm to extract early fault characteristic frequencies of bearings under low SNR. As the morphological parameters of the Laplace wavelet dictionary and sparse coefficients are not easy to obtain, this method uses the adaptive empirical wavelet transform (AEWT) to determine the morphological parameters of the Laplace wavelet. Firstly, AEWT is applied to obtain the different frequency components, and the combination index is utilized for optimal component selection. Then, the morphological parameters of LWD are determined by AEWT processing, by which the overcomplete dictionary that best matches the signal can be obtained. Finally, the optimal sparse representation of the component signal in the dictionary is calculated by FSS, which helps to achieve sparse denoising and enhance the impact features. The effectiveness of the method is verified by simulation. The effectiveness and advantages of LWDFSS-AEWT are verified by experiment in comparison with methods such as fast spectral kurtosis (FSK), correlation filtering (CF), shift-invariant sparse coding (SISC), base pursuit denoising (BPDN) and wavelet packet transform Kurtogram (WPT Kurtogram). Full article
(This article belongs to the Special Issue Machine Condition Monitoring and Fault Diagnosis: From Theory to Application)
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