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Processes
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Research on Intelligent Fault Diagnosis Based on Neural Network
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Research on Intelligent Fault Diagnosis Based on Neural Network

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Automation Control Systems".

Deadline for manuscript submissions: closed (1 February 2025) | Viewed by 5637

Special Issue Editors

Dr. Yanyan Hu

E-Mail Website
Guest Editor
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing 100083, China
Interests: fault diagnosis; remaining useful life prediction; state estimation
Prof. Dr. Lifeng Wu

E-Mail Website
Guest Editor
College of Information Engineering, Capital Normal University, Beijing 100048, China
Interests: fault diagnosis and prediction; deep learning; feature extraction and decision fusion; intelligent detection and predictive control of abnormal working conditions; state detection and feature extraction

Special Issue Information

Dear Colleagues,

Fault diagnosis for complex systems and components has been attracting considerable attention from researchers and engineers, which has greatly contributed to the safety and reliability of various kind of engineering systems. Recent decades have witnessed tremendous success in artificial intelligence- and neural network-related techniques and applications, which has also aroused significant research interests in the field of fault diagnosis. Numerous intelligent fault diagnosis methods have been proposed based on neural networks, achieving great success in diagnostic performance improvement.

This Special Issue on “Research on Intelligent Fault Diagnosis Based on Neural Network” aims to further advance intelligent fault diagnosis methods based on neural network technology and address existing problems in this field. Topics of interest include, but are not limited to, the following:

  • Intelligent algorithms for fault detection, isolation, and estimation;
  • Explainable learning for fault diagnosis;
  • Transferable learning for fault diagnosis;
  • Physics-enhanced machine learning for fault diagnosis;
  • Deep generative model-based fault diagnosis;
  • Knowledge-enhanced machine learning for fault diagnosis;
  • Real-time machine learning for fault diagnosis;
  • Applications of neural network-based intelligent fault diagnosis methods;
  • Fault-tolerant control;
  • Remaining useful life prediction;
  • Intelligent maintenance strategies;
  • Other related topics.

Dr. Yanyan Hu
Prof. Dr. Lifeng Wu
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. Processes 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.

Keywords

  • intelligent fault diagnosis
  • neural networks
  • machine learning
  • fault-tolerant control
  • remaining useful life prediction
  • intelligent maintenance

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Published Papers (8 papers)

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Research

14 pages, 2202 KiB  
Article
Fault Diagnosis of Wire Disconnection in Heater Control System Using One-Dimensional Convolutional Neural Network
by Jiawei Guo, Linfeng Sun, Takahiro Kawaguchi and Seiji Hashimoto
Processes 2025, 13(2), 402; https://doi.org/10.3390/pr13020402 - 3 Feb 2025
Viewed by 108
Abstract
Heaters are critical components in various heating control systems, and their faults are often a primary cause of system failure, drawing significant attention from engineers and researchers. Early and accurate fault diagnosis is crucial to prevent cascading failures. Many diagnostic methods target faults [...] Read more.
Heaters are critical components in various heating control systems, and their faults are often a primary cause of system failure, drawing significant attention from engineers and researchers. Early and accurate fault diagnosis is crucial to prevent cascading failures. Many diagnostic methods target faults under generally stable and simple operating conditions, such as constant load or steady-state temperature. However, real-world scenarios are often complex and variable, involving dynamic loads, nonlinear temperature rises, and other challenges, which limit diagnostic accuracy. To address this issue, this paper proposes an intelligent fault diagnosis model based on a one-dimensional convolutional neural network (CNN), using the heater’s current and voltage as the input to the neural network. The effectiveness and accuracy of the proposed model were validated through experimental data under two different conditions, achieving an average accuracy rate of 98%. The disconnection faults were generated during actual operation and occurred in the early stages, differing significantly from artificially simulated faults, thereby increasing the difficulty of accurate diagnosis. Analysis and comparison of the experimental results demonstrate the feasibility of the intelligent diagnostic model and its high diagnostic accuracy. Full article
(This article belongs to the Special Issue Research on Intelligent Fault Diagnosis Based on Neural Network)
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13 pages, 2133 KiB  
Article
A Series Arc Fault Diagnosis Method Based on an Extreme Learning Machine Model
by Lichun Qi, Takahiro Kawaguchi and Seiji Hashimoto
Processes 2024, 12(12), 2947; https://doi.org/10.3390/pr12122947 - 23 Dec 2024
Viewed by 533
Abstract
In this study, we address the critical issue of accurately detecting series AC arc faults, which are often challenging to identify due to their small fault currents and can lead to devastating electrical fires. We propose an intelligent diagnosis method based on the [...] Read more.
In this study, we address the critical issue of accurately detecting series AC arc faults, which are often challenging to identify due to their small fault currents and can lead to devastating electrical fires. We propose an intelligent diagnosis method based on the extreme learning machine (ELM) model to enhance detection accuracy and real-time monitoring capabilities. Our approach involves collecting high-frequency current signals from 23 types of loads using a self-developed AC series arc fault data acquisition device. We then extract 14 features from both the time and frequency domains as candidates for arc fault diagnosis, employing a random forest to select the most significantly changed features. Finally, we design an ELM classifier for series arc fault diagnosis, achieving an identification accuracy of 99.00% ± 0.26%. Compared to existing series arc fault diagnosis methods, our ELM-based method demonstrates superior recognition performance. This study contributes to the field by providing a more accurate and efficient diagnostic tool for series AC arc faults, with broad implications for electrical safety and fire prevention. Full article
(This article belongs to the Special Issue Research on Intelligent Fault Diagnosis Based on Neural Network)
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14 pages, 6982 KiB  
Article
Deep Learning Integration for Normal Breathing Classification Using a Flexible Fiber Sensor
by Jiseon Kim and Jooyong Kim
Processes 2024, 12(12), 2644; https://doi.org/10.3390/pr12122644 - 24 Nov 2024
Viewed by 859
Abstract
Measuring respiratory parameters is crucial for clinical decision making and detecting abnormal patterns for disease prevention. While deep learning methods are commonly used in respiratory analysis, the image-based classification of abnormal breathing remains limited. This study developed a stitched sensor using silver-coated thread, [...] Read more.
Measuring respiratory parameters is crucial for clinical decision making and detecting abnormal patterns for disease prevention. While deep learning methods are commonly used in respiratory analysis, the image-based classification of abnormal breathing remains limited. This study developed a stitched sensor using silver-coated thread, optimized for the knit fabric’s course direction in a belt configuration. By applying a Continuous Wavelet Transform (CWT) and a two-dimension Convolutional Neural Network (2D-CNN), the model achieved 96% accuracy, with potential for further improvement through data expansion. Full article
(This article belongs to the Special Issue Research on Intelligent Fault Diagnosis Based on Neural Network)
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19 pages, 4108 KiB  
Article
Deep Neural Network Optimization for Efficient Gas Detection Systems in Edge Intelligence Environments
by Amare Mulatie Dehnaw, Ying-Jui Lu, Jiun-Hann Shih, Cheng-Kai Yao, Mekuanint Agegnehu Bitew and Peng-Chun Peng
Processes 2024, 12(12), 2638; https://doi.org/10.3390/pr12122638 - 22 Nov 2024
Viewed by 824
Abstract
This paper introduces an optimized deep neural network (DNN) framework for an efficient gas detection system applicable across various settings. The proposed optimized DNN model addresses key issues in conventional machine learning (ML), including slow computation times, convergence issues, and poor adaptability to [...] Read more.
This paper introduces an optimized deep neural network (DNN) framework for an efficient gas detection system applicable across various settings. The proposed optimized DNN model addresses key issues in conventional machine learning (ML), including slow computation times, convergence issues, and poor adaptability to new data, which can result in increased prediction errors and reduced reliability. The proposed framework methodology comprises four phases: data collection, pre-processing, offline DNN training optimization, and online model testing and deployment. The training datasets are collected from seven classes of liquid beverages and environmental air samples using integrated gas sensor devices and an edge intelligence environment. The proposed DNN algorithm is trained on high-performance computing systems by fine-tuning multiple hyperparameter optimization techniques, resulting in an optimized DNN. This well-trained DNN model is validated using unseen new testing datasets in high-performance computing systems. Experimental results demonstrate that the optimized DNN can accurately recognize different beverages, achieving an impressive detection accuracy rate of 98.29%. The findings indicate that the proposed system significantly enhances gas identification capabilities and effectively addresses the slow computation and performance issues associated with traditional ML methods. This work highlights the potential of optimized DNNs to provide reliable and efficient contactless detection solutions across various industries, enhancing real-time gas detection applications. Full article
(This article belongs to the Special Issue Research on Intelligent Fault Diagnosis Based on Neural Network)
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18 pages, 3727 KiB  
Article
Application of SPEA2-MMBB for Distributed Fault Diagnosis in Nuclear Power System
by Ying Xu, Jie Ma and Jinxiao Yuan
Processes 2024, 12(12), 2620; https://doi.org/10.3390/pr12122620 - 21 Nov 2024
Viewed by 459
Abstract
Accurate fault diagnosis in nuclear power systems is essential for ensuring reactor stability, reducing the risk of potential faults, enhancing system reliability, and maintaining operational safety. Traditional diagnostic methods, especially those based on single-system approaches, struggle to address the complexities of composite faults [...] Read more.
Accurate fault diagnosis in nuclear power systems is essential for ensuring reactor stability, reducing the risk of potential faults, enhancing system reliability, and maintaining operational safety. Traditional diagnostic methods, especially those based on single-system approaches, struggle to address the complexities of composite faults and highly coupled fault data. In this paper, we introduce a distributed fault diagnosis method for nuclear power systems that leverages the Strength Pareto Evolutionary Algorithm 2 (SPEA2) for multi-objective optimization and a modified MobileNetV3 neural network with a Bottleneck Attention Module (MMBB). The SPEA2 algorithm is used to optimize sensor feature selection, and the sensor data are then input into the MMBB model for training. The MMBB model outputs accuracy rates for each subsystem and the overall system, which are subsequently used as optimization targets to guide SPEA2 in refining the sensor selection process for distributed diagnosis. The experimental results demonstrate that this method significantly enhances subsystem accuracy, with an average accuracy of 98.73%, and achieves a comprehensive system accuracy of 95.22%, indicating its superior performance compared to traditional optimization and neural network-based approaches. Full article
(This article belongs to the Special Issue Research on Intelligent Fault Diagnosis Based on Neural Network)
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16 pages, 4637 KiB  
Article
Evaluation of Electrical Properties and Uniformity of Single Wall Carbon Nanotube Dip-Coated Conductive Fabrics Using Convolutional Neural Network-Based Image Analysis
by Erin Kim, SangUn Kim and Jooyong Kim
Processes 2024, 12(11), 2534; https://doi.org/10.3390/pr12112534 - 13 Nov 2024
Viewed by 632
Abstract
This study proposes a convolutional neural network (CNN)-based image analysis method to evaluate the electrical properties and uniformity of conductive fabrics treated with single-walled carbon nanotube (SWCNT) dip-coating. The conductive fabric was produced by dip-coating cotton-blended spandex with SWCNT, and the surface images [...] Read more.
This study proposes a convolutional neural network (CNN)-based image analysis method to evaluate the electrical properties and uniformity of conductive fabrics treated with single-walled carbon nanotube (SWCNT) dip-coating. The conductive fabric was produced by dip-coating cotton-blended spandex with SWCNT, and the surface images were scanned and preprocessed to obtain image data, while resistance measurements were conducted to obtain labels and build the dataset. SEM analysis revealed that as the number of dip-coating cycles increased, particle density and path formation improved. The CNN model learned the relationship between surface images and resistance values, achieving a high predictive performance, with an R-squared (R²) value of 0.9422. The model demonstrated prediction accuracies of 99.1792% for the coefficient of variation (CV) of uniformly coated fabrics and 96.8877% for non-uniformly coated fabrics. Additionally, p-value analysis of all fabric samples yielded a result of 0.96044, indicating no statistically significant difference between the predicted and actual values. The proposed CNN-based model can accurately evaluate the electrical uniformity of conductive fabrics, showing potential for contributing to quality control and process optimization in production. Full article
(This article belongs to the Special Issue Research on Intelligent Fault Diagnosis Based on Neural Network)
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21 pages, 6031 KiB  
Article
FDADNet: Detection of Surface Defects in Wood-Based Panels Based on Frequency Domain Transformation and Adaptive Dynamic Downsampling
by Hongli Li, Zhiqi Yi, Zhibin Wang, Ying Wang, Liang Ge, Wei Cao, Liye Mei, Wei Yang and Qin Sun
Processes 2024, 12(10), 2134; https://doi.org/10.3390/pr12102134 - 30 Sep 2024
Viewed by 816
Abstract
The detection of surface defects on wood-based panels plays a crucial role in product quality control. However, due to the complex background and low contrast of defects in wood-based panel images, features extracted by traditional deep learning methods based on spatial domain processing [...] Read more.
The detection of surface defects on wood-based panels plays a crucial role in product quality control. However, due to the complex background and low contrast of defects in wood-based panel images, features extracted by traditional deep learning methods based on spatial domain processing often contain noise and blurred boundaries, which severely affects detection performance. To address these issues, we have proposed a wood-based panel surface defect detection method based on frequency domain transformation and adaptive dynamic downsampling (FDADNet). Specifically, we designed a Multi-axis Frequency Domain Weighted Information Representation Module (MFDW), which effectively decoupled the indistinguishable low-contrast defects from the background in the transform domain. Gaussian filtering was then employed to eliminate noise and blur between the defects and the background. Additionally, to tackle the issue of scale differences in defects that led to difficulties in accurate capture, we designed an Adaptive Dynamic Convolution (ADConv) module for downsampling. This method flexibly compressed and enhanced features, effectively improving the differentiation of the features of objects of varying scales in the transform space, and ultimately achieved effective defect detection. To compensate for the lack of data, we constructed a dataset of wood-based panel surface defects, WBP-DET. The experimental results showed that the proposed FDADNet effectively improved the detection performance of wood-based panel surface defects in complex scenarios, achieving a solid balance between efficiency and accuracy. Full article
(This article belongs to the Special Issue Research on Intelligent Fault Diagnosis Based on Neural Network)
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13 pages, 1944 KiB  
Article
A Convolutional Neural Network-Based Defect Recognition Method for Power Insulator
by Nan Li, Dejun Zeng, Yun Zhao, Jiahao Wang and Bo Wang
Processes 2024, 12(10), 2129; https://doi.org/10.3390/pr12102129 - 30 Sep 2024
Cited by 1 | Viewed by 703
Abstract
As the scale of the power grid rapidly expands, its operation becomes increasingly complex, with higher demands on personnel proficiency, grid stability, equipment safety, and operational efficiency. In this study, a novel power insulator defect detection method based on convolutional neural networks (CNNs) [...] Read more.
As the scale of the power grid rapidly expands, its operation becomes increasingly complex, with higher demands on personnel proficiency, grid stability, equipment safety, and operational efficiency. In this study, a novel power insulator defect detection method based on convolutional neural networks (CNNs) is proposed. This method innovatively combines the feature extraction advantages of deep learning to build an efficient binary classification model capable of accurately detecting defects in power insulators in complex backgrounds. To avoid the impact of a small dataset on model performance, transfer learning was employed during model training to enhance the model’s generalization ability. A combination of Grid Search and Random Search was used for hyperparameter tuning, and the Early Stopping strategy was introduced to effectively prevent the model from overfitting to the training set, ensuring generalization performance on the validation set. Experimental results show that the proposed method achieves an average accuracy of 98.6%, a recall of 96.8%, and an F1 score of 97.7% on the test set. Compared to traditional Faster RCNN and PCA-SVM methods, the proposed CNN model significantly improves detection accuracy and computational efficiency in complex backgrounds, exhibiting superior recognition precision and model generalization ability for efficiently and accurately identifying defective insulators. Full article
(This article belongs to the Special Issue Research on Intelligent Fault Diagnosis Based on Neural Network)
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