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Applied Sciences
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Machine Learning and Deep Learning-Based Fault Detection and Diagnosis
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Machine Learning and Deep Learning-Based Fault Detection and Diagnosis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (20 July 2024) | Viewed by 8133

Special Issue Editors

Prof. Dr. Hong-Zhong Huang

E-Mail Website
Guest Editor
Center for System Reliability and Safety, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: reliability engineering; optimization design; fuzzy sets theory
Special Issues, Collections and Topics in MDPI journals
Dr. He Li

E-Mail Website
Guest Editor
Centre for Marine Technology and Ocean Engineering (CENTEC) Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal
Interests: system reliability; availability; maintainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern systems tend to be more complicated than ever before, facilitated by novel design concepts and advancements in new technologies such as sensing, materials, communication, and systems (or functions) integrity. Fault detection and diagnosis are the core of healthy state awareness and prediction, as well as fault prevention. Fault detection and diagnosis of modern systems, however, are difficult to implement owing to (i) it being a coupling subject involving performance analysis, sensor placement and communication, data collection and analysis, as well as benefits evaluation and decision-making; and (ii) it requiring a comprehensive and deep understanding of the working states of complicated systems and their interactive mechanisms with variable (even unpredicted, for some cases) environmental factors.

Machine learning and deep learning have emerged and represent promising ways of solving detection and diagnosis problems of modern systems. These novel tools are subverting traditional model-based concepts. To this end, this Special Issue aims to report the state-of-the-art development and applications of machine learning and deep learning-based fault detection and diagnosis, including, but not limited to, sensor configuration design solutions, fault detection, fault diagnosis, fault prognosis, and the subsequent condition-based maintenance and predictive maintenance. Original research and review articles related, but not limited, to the following topics are welcomed:

  • New design concepts of health monitoring platforms.
  • Modeling and analyzing of structures health state.
  • Sensing and monitoring advancement toward structures.
  • Computation and simulation tools.
  • Vibration and its preventions.
  • Advanced methodologies on machine learning and deep learning.
  • Machine learning and deep learning-based fault detection.
  • Machine learning and deep learning-based fault diagnosis.
  • Machine learning and deep learning-based fault prognosis.

Prof. Dr. Hong-Zhong Huang
Dr. He Li
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. 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.

Keywords

  • machine learning
  • deep learning
  • fault detection
  • fault diagnosis

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Published Papers (4 papers)

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Research

22 pages, 10776 KiB  
Article
Fatigue Characteristics Analysis of Carbon Fiber Laminates with Multiple Initial Cracks
by Zheng Liu, Yuhao Zhang, Haodong Liu, Xin Liu, Jinlong Liang and Zhenjiang Shao
Appl. Sci. 2024, 14(18), 8572; https://doi.org/10.3390/app14188572 - 23 Sep 2024
Viewed by 1446
Abstract
In the entire wind turbine system, the blade acts as the central load-bearing element, with its stability and reliability being essential for the safe and effective operation of the wind power unit. Carbon fiber, known for its high strength-to-weight ratio, high modulus, and [...] Read more.
In the entire wind turbine system, the blade acts as the central load-bearing element, with its stability and reliability being essential for the safe and effective operation of the wind power unit. Carbon fiber, known for its high strength-to-weight ratio, high modulus, and lightweight characteristics, is extensively utilized in blade manufacturing due to its superior attributes. Despite these advantages, carbon fiber composites are frequently subjected to cyclic loading, which often results in fatigue issues. The presence of internal manufacturing defects further intensifies these fatigue challenges. Considering this, the current study focuses on carbon fiber composites with multiple pre-existing cracks, conducting both static and fatigue experiments by varying the crack length, the angle between cracks, and the distance among them to understand their influence on the fatigue life under various conditions. Furthermore, this study leverages the advantages of Paris theory combined with the Extended Finite Element Method (XFEM) to simulate cracks of arbitrary shapes, introducing a fatigue simulation method for carbon fiber composite laminates with multiple cracks to analyze their fatigue characteristics. Concurrently, the Particle Swarm Optimization (PSO) algorithm is employed to determine the optimal weight configuration, and the Backpropagation neural network (BP) is used to train and adjust the weights and thresholds to minimize network errors. Building on this foundation, a surrogate model for predicting the fatigue life of carbon fiber composite laminates with multiple cracks under conditions of physical parameter uncertainty has been constructed, achieving modeling and assessment of fatigue reliability. This research offers theoretical insights and methodological guidance for the utilization of carbon fiber-reinforced composites in wind turbine blade applications. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning-Based Fault Detection and Diagnosis)
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17 pages, 3514 KiB  
Article
Optimizing CNN-LSTM for the Localization of False Data Injection Attacks in Power Systems
by Zhuo Li, Yaobin Xie, Rongkuan Ma and Zihan Wei
Appl. Sci. 2024, 14(16), 6865; https://doi.org/10.3390/app14166865 - 6 Aug 2024
Cited by 2 | Viewed by 2449
Abstract
As the informatization of power systems advances, the secure operation of power systems faces various potential network attacks and threats. The false data injection attack (FDIA) is a common attack mode that can lead to abnormal system operations and serious economic losses by [...] Read more.
As the informatization of power systems advances, the secure operation of power systems faces various potential network attacks and threats. The false data injection attack (FDIA) is a common attack mode that can lead to abnormal system operations and serious economic losses by injecting abnormal data into terminal links or devices. The current research on FDIA primarily focuses on detecting its existence, but there is relatively little research on the localization of the attacks. To address this challenge, this study proposes a novel FDIA localization method (GA-CNN-LSTM) that combines convolutional neural networks (CNNs), long short-term memory (LSTM), and a genetic algorithm (GA) and can accurately locate the attacked bus or line. This method utilizes a CNN to extract local features and combines LSTM with time series information to extract global features. It integrates a CNN and LSTM to deeply explore complex patterns and dynamic changes in the data, effectively extract FDIA features in the data, and optimize the hyperparameters of the neural network using the GA to ensure an optimal performance of the model. Simulation experiments were conducted on the IEEE 14-bus and 118-bus test systems. The results indicate that the GA-CNN-LSTM method achieved F1 scores for location identification of 99.71% and 99.10%, respectively, demonstrating superior localization performance compared to other methods. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning-Based Fault Detection and Diagnosis)
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22 pages, 6719 KiB  
Article
Pearson-ShuffleDarkNet37-SE-Fully Connected-Net for Fault Classification of the Electric System of Electric Vehicles
by Quan Lu, Shan Chen, Linfei Yin and Lu Ding
Appl. Sci. 2023, 13(24), 13141; https://doi.org/10.3390/app132413141 - 11 Dec 2023
Cited by 2 | Viewed by 1376
Abstract
As the core components of electric vehicles, the safety of the electric system, including motors, batteries, and electronic control systems, has always been of great concern. To provide early warning of electric-system failure and troubleshoot the problem in time, this study proposes a [...] Read more.
As the core components of electric vehicles, the safety of the electric system, including motors, batteries, and electronic control systems, has always been of great concern. To provide early warning of electric-system failure and troubleshoot the problem in time, this study proposes a novel energy-vehicle electric-system failure-classification method, which is named Pearson-ShuffleDarkNet37-SE-Fully Connected-Net (PSDSEF). Firstly, the raw data were preprocessed and dimensionality reduction was performed after the Pearson correlation coefficient; then, data features were extracted utilizing ShuffleNet and an improved DarkNet37-SE network based on DarkNet53; secondly, the inserted squeeze-and-excitation networks (SE-Net) channel attention were able to obtain more fault-related target information; finally, the prediction results of the ShuffleNet and DarkNet37-SE networks were aggregated with a fully connected neural network to output the classification results. The experimental results showed that the proposed PSDSEF-based electric vehicles electric-system fault-classification method achieved an accuracy of 97.22%, which is better than other classical convolutional neural networks with the highest accuracy of 92.19% (ResNet101); the training time is faster than the average training time of the comparative networks. The proposed PSDSEF has the advantage of high classification accuracy and small number of parameters. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning-Based Fault Detection and Diagnosis)
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19 pages, 959 KiB  
Article
An Approach of Improving the Efficiency of Software Fault Localization based on Feedback Ranking Information
by Bo Yang, Xiaowen Ma, Haoran Guo, Yuze He and Fu Xu
Appl. Sci. 2023, 13(18), 10351; https://doi.org/10.3390/app131810351 - 15 Sep 2023
Cited by 1 | Viewed by 1607
Abstract
Fault localization, a critical process of software debugging, can be implemented by ranking program statements according to their suspiciousness of being faulty, which, in turn, is calculated based on the execution behaviors of test cases. The performance of fault localization will deteriorate if [...] Read more.
Fault localization, a critical process of software debugging, can be implemented by ranking program statements according to their suspiciousness of being faulty, which, in turn, is calculated based on the execution behaviors of test cases. The performance of fault localization will deteriorate if the actual faulty statement is ranked low in suspiciousness. Intuitively speaking, the quality of the used test cases affects the suspiciousness ranking and thus the efficacy of fault localization. As such, it is necessary to generate test cases with “better” quality to improve the chance of faulty statements being ranked as highly suspicious. In this paper, we propose a software fault localization approach based on feedback ranking information, namely FLFR, according to an improved genetic algorithm. The starting point of the new method is the execution of a set of test cases, which gives a preliminary suspiciousness ranking of program statements. The improved genetic algorithm is iteratively applied to generate new test cases. The new method is evaluated through a series of experiments on four C programs and two Java programs. The experimental results show that the test cases automatically generated by the method can improve the suspiciousness ranking of the faulty statement, and thus enhance the performance of fault localization. Full article
(This article belongs to the Special Issue Machine Learning and Deep Learning-Based Fault Detection and Diagnosis)
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