Advances in Fault Diagnosis and Anomaly Detection

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 10756

Special Issue Editors


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Guest Editor
Department of Automation, Tsinghua University, Beijing 100084, China
Interests: modeling and fault monitoring of complex industrial systems; computational energy intelligence
Department of Chemical Engineering, Texas Tech University, 807 Canton Ave., Lubbock, 79409 TX, USA
Interests: modeling and control of complex systems; process monitoring; fault detection and diagnosis
Department of Aeronautical and Automotive Engineering Loughborough University, Loughborough, Leicester LE11 3TU, UK
Interests: state estimation; operational reliability; data analysis; target tracking

Special Issue Information

Dear Colleagues,

Modern industrial processes and energy systems, such as chemical reaction processes and grid-scale battery storage systems, are large-scale with interconnected units that are expensive to operate and vulnerable to faults. Any incipient and component fault may propagate to the entire system, causing system shutdown or interruption and thus leading to an enormous loss of profits and services. It is of great significance to monitor the operation of systems for early and reliably detecting and diagnosing system anomalies. Therefore, fault diagnosis has witnessed phenomenal progress from both academia and industry over the last few decades for monitoring increasingly complex systems. Such advancements are largely attributed to the emerging industrial and energy systems operating data together with the rapid integration of machine learning, deep learning, and data science techniques with fault diagnosis.

The goal of this Special Issue is to aggregate latest research outcomes in the field of advanced fault diagnosis contributing to methodology advancement, algorithm development, and practical applications. Interested authors are invited to submit high-quality papers on topics including but not limited to:

  • Statistical machine learning and dimension reduction for fault diagnosis and anomaly detection;
  • Fault diagnosis based on big data, deep learning, and AI techniques;
  • Model-based fault detection and diagnosis;
  • Prognostics, predictive maintenance and remaining useful life (RUL) prediction;
  • Applications to energy systems and industrial processes

Dr. Benben Jiang
Dr. Qiugang Lu
Dr. Yang Liu
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. Machines is an international peer-reviewed open access monthly 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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Research

16 pages, 2746 KiB  
Article
A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults
by Tarek Berghout, Toufik Bentrcia, Wei Hong Lim and Mohamed Benbouzid
Machines 2023, 11(12), 1089; https://doi.org/10.3390/machines11121089 - 14 Dec 2023
Viewed by 1369
Abstract
The deep learning diagnosis of aircraft engine-bearing faults enables cost-effective predictive maintenance while playing an important role in increasing the safety, reliability, and efficiency of aircraft operations. Because of highly dynamic and harsh operating conditions of this system, such modeling is challenging due [...] Read more.
The deep learning diagnosis of aircraft engine-bearing faults enables cost-effective predictive maintenance while playing an important role in increasing the safety, reliability, and efficiency of aircraft operations. Because of highly dynamic and harsh operating conditions of this system, such modeling is challenging due to data complexity and drift, making it difficult to reveal failure patterns. As a result, the objective of this study is dual. To begin, a highly structured data preprocessing strategy ranging from extraction, denoising, outlier removal, scaling, and balancing is provided to solve data complexity that resides specifically in outliers, noise, and data imbalance problems. Gap statistics under k-means clustering are used to evaluate preprocessing results, providing a quantitative estimate of the ideal number of clusters and thereby enhancing data representations. This is the first time, to the best of authors’ knowledge, that such a criterion has been employed for an important step in a preliminary ground truth validation in supervised learning. Furthermore, to tackle data drift issues, long-short term memory (LSTM) adaptive learning features are used and subjected to a learning parameter improvement method utilizing recursive weights initialization (RWI) across several rounds. The strength of such methodology can be seen by application to realistic, extremely new, complex, and dynamic data collected from a real test-bench. Cross validation of a single LSTM layer model with only 10 neurons shows its ability to enhance classification performance by 7.7508% over state-of-the-art results, obtaining a classification accuracy of 92.03 ± 0.0849%, which is an exceptional performance in such a benchmark. Full article
(This article belongs to the Special Issue Advances in Fault Diagnosis and Anomaly Detection)
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17 pages, 7699 KiB  
Article
A New Automated Classification Framework for Gear Fault Diagnosis Using Fourier–Bessel Domain-Based Empirical Wavelet Transform
by Dada Saheb Ramteke, Anand Parey and Ram Bilas Pachori
Machines 2023, 11(12), 1055; https://doi.org/10.3390/machines11121055 - 28 Nov 2023
Cited by 4 | Viewed by 962
Abstract
Gears are the most important parts of a rotary system, and they are used for mechanical power transmission. The health monitoring of such a system is needed to observe its effective and reliable working. An approach that is based on vibration is typically [...] Read more.
Gears are the most important parts of a rotary system, and they are used for mechanical power transmission. The health monitoring of such a system is needed to observe its effective and reliable working. An approach that is based on vibration is typically utilized while carrying out fault diagnostics on a gearbox. Using the Fourier–Bessel series expansion (FBSE) as the basis for an empirical wavelet transform (EWT), a novel automated technique has been proposed in this paper, with a combination of these two approaches, i.e., FBSE-EWT. To improve the frequency resolution, the current empirical wavelet transform will be reformed utilizing the FBSE technique. The proposed novel method includes the decomposition of different levels of gear crack vibration signals into narrow-band components (NBCs) or sub-bands. The Kruskal–Wallis test is utilized to choose the features that are statistically significant in order to separate them from the sub-bands. Three classifiers are used for fault classification, i.e., random forest, J48 decision tree classifiers, and multilayer perceptron function classifier. A comparative study has been performed between the existing EWT and the proposed novel methodology. It has been observed that the FBSE-EWT with a random forest classifier shows a better gear fault detection performance compared to the existing EWT. Full article
(This article belongs to the Special Issue Advances in Fault Diagnosis and Anomaly Detection)
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20 pages, 10610 KiB  
Article
Comparative Study on Health Monitoring of a Marine Engine Using Multivariate Physics-Based Models and Unsupervised Data-Driven Models
by Chao Fu, Xiaoxia Liang, Qian Li, Kuan Lu, Fengshou Gu, Andrew D. Ball and Zhaoli Zheng
Machines 2023, 11(5), 557; https://doi.org/10.3390/machines11050557 - 15 May 2023
Cited by 2 | Viewed by 1213
Abstract
The marine engine is a complex-structured multidisciplinary system that operates in a harsh environment involving high temperatures and pressures and gas/fluid/solid interactions. Many malfunctions and faults can occur to the marine engine and efficient condition monitoring is critical to ensure the expected performance. [...] Read more.
The marine engine is a complex-structured multidisciplinary system that operates in a harsh environment involving high temperatures and pressures and gas/fluid/solid interactions. Many malfunctions and faults can occur to the marine engine and efficient condition monitoring is critical to ensure the expected performance. In this paper, a marine engine test rig is established and its process data are recorded, including various temperatures and pressures. Two data-driven models, i.e., principal component analysis and the sparse autoencoder, and a physics-based model are applied to the marine engine for two classic faults, i.e., lubrication oil filter blocking and cylinder leakage. Comparative studies and discussions are conducted regarding their performance in terms of anomaly detection and fault isolation. The data points collected for the filter blocking fault are generally two times higher than the fault thresholds set by the data-driven models. In the physics-based model, it is observed that the lubrication oil pressure falls from the predicted 3.2–3.8 bar to around 2.3 bar. For the cylinder leakage fault, the fault test data are nearly four times higher than the thresholds in the data-driven models. The exhaust gas temperature of the leaked cylinder falls from an estimated 150–200 °C to about 100 °C. The transferability and interpretability of these models are finally discussed. The findings of the present study offer insights into the two types of models and can provide guidance for the effective condition monitoring of marine engines. Full article
(This article belongs to the Special Issue Advances in Fault Diagnosis and Anomaly Detection)
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26 pages, 1317 KiB  
Article
Evaluation of Different Fault Diagnosis Methods and Their Applications in Vehicle Systems
by Shiqing Li, Michael Frey and Frank Gauterin
Machines 2023, 11(4), 482; https://doi.org/10.3390/machines11040482 - 17 Apr 2023
Viewed by 2031
Abstract
A high level of automation in vehicles is accompanied by a variety of sensors and actuators, whose malfunctions must be dealt with caution because they might cause serious driving safety hazards. Therefore, a robust and highly accurate fault detection and diagnosis system to [...] Read more.
A high level of automation in vehicles is accompanied by a variety of sensors and actuators, whose malfunctions must be dealt with caution because they might cause serious driving safety hazards. Therefore, a robust and highly accurate fault detection and diagnosis system to monitor the operational states of vehicle systems is an indispensable prerequisite. In the area of fault diagnosis, numerous techniques have been studied, and each one has pros and cons. Selecting the best approach based on the requirements or usage scenarios will save much needless work. In this article, the authors examine some of the most common fault diagnosis methods for their applicability to automated vehicle systems: the traditional binary logic method, the fuzzy logic method, the fuzzy neural method, and two neural network methods (the feedforward neural network and the convolutional neural network). For each approach, the diagnosis algorithms for vehicle systems were modeled differently. The analysis of the detection capabilities and the suitable application scenarios of each fault diagnosis approach for vehicle systems, as well as recommendations for selecting different methods for various diagnosis needs, are also provided. In the future, this can serve as an effective guide for the selection of a suitable fault diagnosis approach based on the application scenarios for vehicle systems. Full article
(This article belongs to the Special Issue Advances in Fault Diagnosis and Anomaly Detection)
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20 pages, 6489 KiB  
Article
Fault Prediction of On-Board Train Control Equipment Using a CGAN-Enhanced XGBoost Method with Unbalanced Samples
by Jiang Liu, Kangzhi Xu, Baigen Cai and Zhongbin Guo
Machines 2023, 11(1), 114; https://doi.org/10.3390/machines11010114 - 14 Jan 2023
Cited by 2 | Viewed by 1895
Abstract
On-board train control equipment is an important component of the Train Control System (TCS) of railway trains. In order to guarantee the safe and efficient operation of the railway system, Predictive Maintenance (PdM) is significantly required. The operation data of the on-board equipment [...] Read more.
On-board train control equipment is an important component of the Train Control System (TCS) of railway trains. In order to guarantee the safe and efficient operation of the railway system, Predictive Maintenance (PdM) is significantly required. The operation data of the on-board equipment allow us to build fault prediction models using a data-driven approach. However, the problem of unbalanced fault samples makes it difficult to achieve the expected modeling performance. In this paper, a Conditional Generative Adversarial Network (CGAN) is adopted to solve the unbalancing problem by generating synthetic samples corresponding to specific fault labels that belong to the minority classes. With this basis, a CGAN-enhanced eXtreme Gradient Boosting (XGBoost) solution is presented for training the fault prediction models. From the pre-processing to the field data, artificial fault samples are generated and integrated into the training sample sets, and the XGBoost models can be derived with multiple decision trees. Both the feature importance sequence list and the knowledge graph are derived to describe the characteristics obtained by the models. Filed data sets from practical operation are utilized to validate the proposed solution. By comparison with conventional machine learning algorithms, it can be found that higher accuracy, precision, recall, and F1 scores, which are up to 99.76%, can be achieved by the proposed solution. By involving the CGAN strategy, the maximum enhancement to the F1 score with the XGBoost approach reaches 6.13%. The advantages of the proposed solution show great potential in implementing equipment health management and intelligent condition-based maintenance. Full article
(This article belongs to the Special Issue Advances in Fault Diagnosis and Anomaly Detection)
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18 pages, 3092 KiB  
Article
Comprehensive Learning Particle Swarm Optimized Fuzzy Petri Net for Motor-Bearing Fault Diagnosis
by Chuannuo Xu, Jiming Li and Xuezhen Cheng
Machines 2022, 10(11), 1022; https://doi.org/10.3390/machines10111022 - 03 Nov 2022
Cited by 2 | Viewed by 1080
Abstract
Petri net is a widely used fault-diagnosis algorithm. However, it presents poor fault-diagnosis effectiveness and accuracy caused by the parameter setting and adjustment, depending entirely on expert experience in a system with a single input signal type. To address this problem, a comprehensive [...] Read more.
Petri net is a widely used fault-diagnosis algorithm. However, it presents poor fault-diagnosis effectiveness and accuracy caused by the parameter setting and adjustment, depending entirely on expert experience in a system with a single input signal type. To address this problem, a comprehensive learning particle swarm optimized fuzzy Petri net (CLPSO-FPN) algorithm is proposed for motor-bearing fault diagnosis. CLPSO is employed to obtain an adaptive system parameter set to reduce the fault-diagnosis error caused by human subjective factors. Moreover, a new proposed concept of the transition influence factor replaces the traditional transition confidence to improve the nonlinear expression ability of traditional Petri nets, which suppresses the space explosion problem of the fault-diagnosis model. Finally, experiments are implemented on a dataset of motor bearings. Compared with traditional faults diagnosis methods, the proposed method realized better performance in the fault location and prediction functions of motor bearings, which is beneficial for troubleshooting and motor maintenance. Full article
(This article belongs to the Special Issue Advances in Fault Diagnosis and Anomaly Detection)
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20 pages, 6103 KiB  
Article
Fault Feature Enhanced Extraction and Fault Diagnosis Method of Vibrating Screen Bearings
by Xiaohan Cheng, Hui Yang, Long Yuan, Yuxin Lu, Congjie Cao and Guangqiang Wu
Machines 2022, 10(11), 1007; https://doi.org/10.3390/machines10111007 - 01 Nov 2022
Cited by 3 | Viewed by 1348
Abstract
For mechanical equipment, bearings have a high incidence area of faults. A problem for bearings is that their fault characteristics include a vibrating screen exciter which is weak and thus easily covered in strong background noise, hence making the noise difficult to remove. [...] Read more.
For mechanical equipment, bearings have a high incidence area of faults. A problem for bearings is that their fault characteristics include a vibrating screen exciter which is weak and thus easily covered in strong background noise, hence making the noise difficult to remove. In this paper, a noise reduction method based on singular value decomposition, improved by singular value’s unilateral ascent method (SSVD), and a fault feature enhancement method, i.e., variational mode decomposition, improved by revised whale algorithm optimization (RWOA-VMD), are proposed. These two methods are used in vibration signal processing with early faults of bearings having a vibrating screen and they have achieved significant application results. This paper also aims to construct a multi-modal feature matrix composed of energy entropy, singular value entropy, and power spectrum entropy, and then the early fault diagnosis of bearings of a vibrating screen exciter bearing is realized by using the proposed support vector machine, improved by the aquila optimizer algorithm (AO-SVM). Full article
(This article belongs to the Special Issue Advances in Fault Diagnosis and Anomaly Detection)
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