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Artificial-Intelligence-Driven Intelligent Fault Prediction and Health Management Techniques in Manufacturing Systems: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 4489

Special Issue Editors

Prof. Dr. Long Wen

E-Mail Website
Guest Editor
School of Mechanical Engineering and Electronic Information, China University of Geosciences, Wuhan 430074, China
Interests: deep learning; automatic machine learning; fault diagnosis; intelligent algorithm
Special Issues, Collections and Topics in MDPI journals
Dr. Zhuyun Chen

E-Mail Website
Guest Editor
State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment, Guangdong University of Technology, Guangzhou, China
Interests: deep transfer learning; federated learning; signal processing; fault diagnosis
Prof. Dr. Chuanjiang Li

E-Mail Website
Guest Editor
State Key Laboratory of Public Big Data, Gui Zhou University, Guizhou, China
Interests: intelligent PHM; few-shot fault diagnosis; UAV data analysis; meta-learning
Special Issues, Collections and Topics in MDPI journals
Dr. Junyu Qi

E-Mail Website
Guest Editor
Reutlingen Energy Centre, Reutlingen University, 72762 Reutlingen, Germany
Interests: fault detection of wind turbine gearboxes
Prof. Dr. Gernot Schullerus

E-Mail Website
Guest Editor
Reutlingen Energy Centre, Reutlingen University, 72762 Reutlingen, Germany
Interests: energy efficient control of induction machines, optimal control of electrical drives, and condition monitoring
Prof. Dr. Jianan Wei

E-Mail Website
Guest Editor
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang 550025, China
Interests: manufacturing big data and manufacturing information systems; intelligent manufacturing; machine learning; deep transfer learning; fault diagnosis; imbalanced data processing and predictive maintenance
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the current era, the rapid progress of artificial intelligence (AI) has led to the implementation of various AI techniques to ensure equipment and production reliability, safety, and quality, as well as to prevent unexpected failures within smart manufacturing systems. The widespread application of AI techniques presents new opportunities in the realm of smart manufacturing, particularly in the domains of intelligent fault diagnosis, prognosis, and surface defect detection. These AI-supported approaches are proficient in analyzing industrial signals or images to monitor the health and functionality of machines or products, showcasing significant potential to enhance the safety and efficiency of smart manufacturing practices. The proposed Special Issue on artificial-intelligence-driven intelligent fault prediction and health management techniques in manufacturing systems is dedicated to exploring the theories, methodologies, and practical applications of AI techniques within smart manufacturing environments. Researchers are encouraged to leverage various industrial data sources, such as signals, images, or videos, to diagnose and predict the operational status of machines and products.

This Special Issue aims to explore innovative applications of AI in the domains of intelligent fault diagnosis, prognosis, and surface defect detection. We invite contributions that delve into the theoretical foundations, methodological frameworks, and practical implementations of AI-driven techniques in manufacturing systems.

Topics of interest include but are not limited to the following:

  • AI applications in intelligent fault diagnosis and prediction;
  • AI-supported industrial signal and image analysis;
  • AI-driven machine and product health management methods;
  • AI-driven fault prediction and health management techniques in smart manufacturing systems;
  • Industrial big data analytics and AI fusion in smart manufacturing systems.

Prof. Dr. Long Wen
Dr. Zhuyun Chen
Prof. Dr. Chuanjiang Li
Dr. Junyu Qi
Prof. Dr. Gernot Schullerus
Prof. Dr. Jianan Wei
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. Sensors 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 2600 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

  • fault prediction
  • defect detection
  • artificial intelligence
  • smart manufacturing

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Related Special Issue

Published Papers (6 papers)

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Research

18 pages, 5643 KiB  
Article
A New Hybrid Reinforcement Learning with Artificial Potential Field Method for UAV Target Search
by Fang Jin, Zhihao Ye, Mengxue Li, Han Xiao, Weiliang Zeng and Long Wen
Sensors 2025, 25(9), 2796; https://doi.org/10.3390/s25092796 - 29 Apr 2025
Viewed by 308
Abstract
Autonomous navigation and target search for unmanned aerial vehicles (UAVs) have extensive application potential in search and rescue, surveillance, and environmental monitoring. Reinforcement learning (RL) has demonstrated excellent performance in real-time UAV navigation through dynamic optimization of decision-making strategies, but its application in [...] Read more.
Autonomous navigation and target search for unmanned aerial vehicles (UAVs) have extensive application potential in search and rescue, surveillance, and environmental monitoring. Reinforcement learning (RL) has demonstrated excellent performance in real-time UAV navigation through dynamic optimization of decision-making strategies, but its application in large-scale environments for target search and obstacle avoidance is still limited by slow convergence and low computational efficiency. To address this issue, a hybrid framework combining RL and artificial potential field (APF) is proposed to improve the target search algorithm. Firstly, a task scenario and training environment for UAV target search are constructed. Secondly, RL is integrated with APF to form a framework that combines global and local strategies. Thirdly, the hybrid framework is compared with standalone RL algorithms through training and analysis of their performance differences. The experimental results demonstrate that the proposed method significantly outperforms standalone RL algorithms in terms of target search efficiency and obstacle avoidance performance. Specifically, the SAC-APF hybrid framework achieves a 161% improvement in success rate compared to the baseline SAC model, increasing from 0.282 to 0.736 in obstacle scenarios. Full article
(This article belongs to the Special Issue Artificial-Intelligence-Driven Intelligent Fault Prediction and Health Management Techniques in Manufacturing Systems: 2nd Edition)
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18 pages, 2984 KiB  
Article
A Domain Adaptation Meta-Relation Network for Knowledge Transfer from Human-Induced Faults to Natural Faults in Bearing Fault Diagnosis
by Dong Sun, Xudong Yang and Hai Yang
Sensors 2025, 25(7), 2254; https://doi.org/10.3390/s25072254 - 3 Apr 2025
Viewed by 336
Abstract
Intelligent fault diagnosis of bearings is crucial to the safe operation and productivity of mechanical equipment, but it still faces the challenge of difficulty in acquiring real fault data in practical applications. Therefore, this paper proposes a domain adaptive meta-relation network (DAMRN) to [...] Read more.
Intelligent fault diagnosis of bearings is crucial to the safe operation and productivity of mechanical equipment, but it still faces the challenge of difficulty in acquiring real fault data in practical applications. Therefore, this paper proposes a domain adaptive meta-relation network (DAMRN) to achieve diagnostic knowledge transfer from laboratory-simulated faults (human-induced faults) to real scenario faults (natural faults) by fusing meta-learning and domain adaptation techniques. Specifically, firstly, through meta-task scenario training, DAMRN captures task-independent generic features from human-induced fault samples, which gives the model the ability to adapt quickly to the target domain tasks. Secondly, a domain adaptation strategy that complements each other with explicit alignment and implicit confrontation is set up to effectively reduce the domain discrepancy between human-induced faults and natural faults. Finally, this paper experimentally validates DAMRN in two cases (same-machine and cross-machine) of a human-induced fault to a natural fault, and DAMRN outperforms other methods with average accuracies as high as 99.62% and 96.38%, respectively. The success of DAMRN provides a viable solution for practical industrial applications of bearing fault diagnosis. Full article
(This article belongs to the Special Issue Artificial-Intelligence-Driven Intelligent Fault Prediction and Health Management Techniques in Manufacturing Systems: 2nd Edition)
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23 pages, 12338 KiB  
Article
Learning in Two-Scales Through LSTM-GPT2 Fusion Network: A Hybrid Approach for Time Series Anomaly Detection
by Taoyu Wang, Dan Wu, Jun Wang, Jinwei Zhao, Haoming Wang, Dongnan Xie, Hongtao Zhang and Xinhong Hei
Sensors 2025, 25(6), 1849; https://doi.org/10.3390/s25061849 - 16 Mar 2025
Viewed by 613
Abstract
Anomaly detection (AD) in multivariate time series data (MTS) collected by industrial sensors is a crucial undertaking for the damage estimation and damage monitoring of machinery like rocket engines, wind turbine blades, and aircraft turbines. Due to the complex structure of industrial systems [...] Read more.
Anomaly detection (AD) in multivariate time series data (MTS) collected by industrial sensors is a crucial undertaking for the damage estimation and damage monitoring of machinery like rocket engines, wind turbine blades, and aircraft turbines. Due to the complex structure of industrial systems and the varying working environments, the collected MTS often contain a significant amount of noise. Current AD studies mostly depend on extracting features from data to obtain the information associated with various working states, and they attempt to detect the abnormal states in the space of the original data. Nevertheless, the latent space, which includes the most essential knowledge learned by the network, is often overlooked. In this paper, a multi-scale feature extraction and data reconstruction deep learning neural network, designated as LGFN, is proposed. It is specifically designed to detect anomalies in MTS in both the original input space and the latent space. In the experimental section, a comparison is made between the proposed AD process and five well-acknowledged AD methods on five public MTS datasets. The outcomes demonstrate that the proposed method attains state-of-the-art or comparable performance. The memory usage experiment illustrates the space efficiency of LGFN in comparison to another AD method based on GPT-2. The ablation studies emphasise the indispensable role of each module in the proposed AD process. Full article
(This article belongs to the Special Issue Artificial-Intelligence-Driven Intelligent Fault Prediction and Health Management Techniques in Manufacturing Systems: 2nd Edition)
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26 pages, 5523 KiB  
Article
A MID-1DC+LRT Multi-Task Model for SOH Assessment and RUL Prediction of Mechanical Systems
by Hai Yang, Xudong Yang, Dong Sun and Yunjin Hu
Sensors 2025, 25(5), 1368; https://doi.org/10.3390/s25051368 - 23 Feb 2025
Viewed by 586
Abstract
Predictive health management (PHM) plays a pivotal role in the maintenance of contemporary industrial systems, with the evaluation of the state of health (SOH) and the prediction of remaining useful life (RUL) constituting its central objectives. Nevertheless, existing studies frequently approach these tasks [...] Read more.
Predictive health management (PHM) plays a pivotal role in the maintenance of contemporary industrial systems, with the evaluation of the state of health (SOH) and the prediction of remaining useful life (RUL) constituting its central objectives. Nevertheless, existing studies frequently approach these tasks in isolation, overlooking their interdependence, and predominantly concentrate on single-condition settings. While Transformers have demonstrated exceptional performance in RUL prediction, their substantial parameter requirements pose challenges to computational efficiency and practical implementation. Further, multi-task learning (MTL) models often experience performance deterioration as a result of imbalanced weighting in their loss functions. To address these challenges, the MID-1DC+LRT model was proposed in the present study. The proposed model integrates a multi-input data 1D convolutional neural network (1D-CNN) and low-rank transformer (LRT) within an MTL framework. This model processes high-dimensional sensor data, multi-condition data, and health indicator data, optimizing the Transformer structure to reduce computational complexity. A homoscedastic uncertainty-based method dynamically adjusts multi-task loss function weights, improving task collaboration and model generalization. The results demonstrate that the proposed model significantly outperformed existing methods in SOH assessment and RUL prediction under multi-condition scenarios, demonstrating superior prediction accuracy and computational efficiency, especially in complex and dynamic environments. Full article
(This article belongs to the Special Issue Artificial-Intelligence-Driven Intelligent Fault Prediction and Health Management Techniques in Manufacturing Systems: 2nd Edition)
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Graphical abstract

18 pages, 2376 KiB  
Article
Remaining Useful Life Prediction of Rolling Bearings Based on CBAM-CNN-LSTM
by Bo Sun, Wenting Hu, Hao Wang, Lei Wang and Chengyang Deng
Sensors 2025, 25(2), 554; https://doi.org/10.3390/s25020554 - 19 Jan 2025
Cited by 1 | Viewed by 1144
Abstract
Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address the problem that Convolutional Neural Networks [...] Read more.
Predicting the Remaining Useful Life (RUL) is vital for ensuring the reliability and safety of equipment and components. This study introduces a novel method for predicting RUL that utilizes the Convolutional Block Attention Module (CBAM) to address the problem that Convolutional Neural Networks (CNNs) do not effectively leverage data channel features and spatial features in residual life prediction. Firstly, Fast Fourier Transform (FFT) is applied to convert the data into the frequency domain. The resulting frequency domain data is then used as input to the convolutional neural network for feature extraction; Then, the weights of channel features and spatial features are assigned to the extracted features by CBAM, and the weighted features are then input into the Long Short-Term Memory (LSTM) network to learn temporal features. Finally, the effectiveness of the proposed model is verified using the PHM2012 bearing dataset. Compared to several existing RUL prediction methods, the mean squared error, mean absolute error, and root mean squared error of the proposed method in this paper are reduced by 53%, 16.87%, and 31.68%, respectively, which verifies the superiority of the method. Meanwhile, the experimental results demonstrate that the proposed method achieves good RUL prediction accuracy across various failure modes. Full article
(This article belongs to the Special Issue Artificial-Intelligence-Driven Intelligent Fault Prediction and Health Management Techniques in Manufacturing Systems: 2nd Edition)
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23 pages, 8620 KiB  
Article
An Attention-Based Multidimensional Fault Information Sharing Framework for Bearing Fault Diagnosis
by Yunjin Hu, Qingsheng Xie, Xudong Yang, Hai Yang and Yizong Zhang
Sensors 2025, 25(1), 224; https://doi.org/10.3390/s25010224 - 3 Jan 2025
Cited by 1 | Viewed by 764
Abstract
Deep learning has performed well in feature extraction and pattern recognition and has been widely studied in the field of fault diagnosis. However, in practical engineering applications, the lack of sample size limits the potential of deep learning in fault diagnosis. Moreover, in [...] Read more.
Deep learning has performed well in feature extraction and pattern recognition and has been widely studied in the field of fault diagnosis. However, in practical engineering applications, the lack of sample size limits the potential of deep learning in fault diagnosis. Moreover, in engineering practice, it is usually necessary to obtain multidimensional fault information (such as fault localization and quantification), while current methods mostly only provide single-dimensional information. Aiming at the above problems, this paper proposes an Attention-based Multidimensional Fault Information Sharing (AMFIS) framework, which aims to overcome the difficulties of multidimensional bearing fault diagnosis in a small sample environment. Specifically, firstly, a shared network is designed to capture the common knowledge of the Fault Localization Task (FLT) and the Fault Quantification Task (FQT) and save it to the global feature pool. Secondly, two branching networks for performing FLT and FQT were constructed, and an attentional mechanism (AM) was used to filter out features from the shared network that were more relevant to the task to enhance the branching network’s capability under small samples. Meanwhile, we propose an innovative Dynamic Adjustment Strategy (DAS) designed to adaptively regulate the training weights of FLT and FQT tasks to achieve optimal training results. Finally, extensive experiments are conducted in two cases to verify the effectiveness and superiority of AMFIS. Full article
(This article belongs to the Special Issue Artificial-Intelligence-Driven Intelligent Fault Prediction and Health Management Techniques in Manufacturing Systems: 2nd Edition)
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