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Machines
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Fault Diagnosis and Fault Tolerant Control in Mechanical System
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Fault Diagnosis and Fault Tolerant Control in Mechanical System

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 4706

Special Issue Editor

Dr. Zhen Jia

E-Mail Website
Guest Editor
School of Civil Aviation, Northwestern Polytechnical University, Xi’an, China
Interests: incremental fault diagnosis; fault-tolerant control; physics-informed neural networks

Special Issue Information

Dear Colleagues,

Fault diagnosis and fault-tolerant control (FTC) technologies are pivotal for ensuring the safety, reliability, and operational continuity of modern mechanical systems under performance degradation or component failures. With the advent of Industry 4.0, these fields are experiencing revolutionary transformations through the integration of advanced sensing, artificial intelligence, and cyber–physical system architectures. The synergy of data-driven diagnostics and resilient control strategies enables mechanical systems to autonomously detect incipient faults, reconfigure control actions, and maintain operational integrity in critical applications—from aerospace propulsion to robotic manufacturing. This Special Issue aims to showcase pioneering research and practical innovations in fault diagnosis and FTC for mechanical systems. We invite contributions addressing the latest methodologies, theoretical breakthroughs, and industrial implementations. Topics of interest include, but are not limited to, the following: AI-enhanced fault identification: deep transfer learning, few-shot learning, and physics-informed neural networks for limited data scenarios. Resilient control architectures: self-healing control, adaptive sliding-mode FTC, and distributed FTC for multi-agent systems. Digital twin-enabled solutions: real-time virtual replicas for fault simulation, prognosis, and control reconfiguration. Cross-domain fusion techniques: multi-sensor fusion (vibration, thermal, acoustic) and heterogeneous data integration under variable operating conditions.

Dr. Zhen Jia
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 250 words) can be sent to the Editorial Office for assessment.

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.

Keywords

  • fault diagnosis
  • fault-tolerant control
  • digital twins
  • physics-informed neural networks
  • multi-sensor fusion
  • cyber–physical systems
  • resilient control transfer learning

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Published Papers (6 papers)

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Research

25 pages, 14486 KB  
Article
A Policy Gradient-Based Improved KAN Convolutional Network Architecture for Fault Diagnosis of Aircraft Hydraulic Systems
by Jing Qu, Cunbao Ma and Zhiyu She
Machines 2026, 14(3), 320; https://doi.org/10.3390/machines14030320 - 12 Mar 2026
Viewed by 233
Abstract
As key power components in aviation machinery, airborne hydraulic systems exhibit significant coupling, nonlinearity, and strong noise interference, which pose enormous challenges for their mechanical fault diagnosis—an essential link in ensuring aviation mechanical system reliability. To address this issue, a policy gradient-based optimization [...] Read more.
As key power components in aviation machinery, airborne hydraulic systems exhibit significant coupling, nonlinearity, and strong noise interference, which pose enormous challenges for their mechanical fault diagnosis—an essential link in ensuring aviation mechanical system reliability. To address this issue, a policy gradient-based optimization method is proposed to autonomously tune network parameters, aiming to enhance the accuracy and robustness of mechanical fault diagnosis. Initially, a KAN (Kolmogorov–Arnold Network) convolution submodel is adopted to strengthen the extraction of weak mechanical fault features from complex hydraulic signals. Subsequently, the policy gradient methodology is employed to iteratively refine the overall network configuration, enabling adaptive optimization of fault diagnosis-related parameters. Extensive experiments on standard hydraulic system datasets demonstrate that the proposed approach outperforms other mainstream intelligent mechanical fault diagnosis methods in terms of diagnostic accuracy, anti-interference ability, and generalization performance. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault Tolerant Control in Mechanical System)
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19 pages, 2985 KB  
Article
Intelligent Diagnosis Method for Bearing Condition Changes Based on Domain Adaptation with Unlabeled Samples
by Pengping Luo and Zhiwei Liu
Machines 2026, 14(3), 294; https://doi.org/10.3390/machines14030294 - 5 Mar 2026
Viewed by 368
Abstract
In the intelligent operation and maintenance of industrial equipment, labeling failure data remains a challenging task due to its high cost and low efficiency. Although incorporating a large amount of unlabeled data alongside limited labeled samples can partially alleviate this “labeling bottleneck,” the [...] Read more.
In the intelligent operation and maintenance of industrial equipment, labeling failure data remains a challenging task due to its high cost and low efficiency. Although incorporating a large amount of unlabeled data alongside limited labeled samples can partially alleviate this “labeling bottleneck,” the performance and robustness of models still heavily depend on the scale and quality of annotated data, which often leads to generalization issues in real industrial scenarios. To address these challenges, this paper proposes an unsupervised fault diagnosis method based on an efficient domain adaptation model named E-DANNMK. This approach reduces reliance on manually labeled fault data, thereby mitigating annotation-related issues such as high cost and potential bias. The E-DANNMK model integrates residual networks, an efficient channel attention mechanism, and domain adversarial neural networks to improve both feature discriminability and cross-domain adaptability. To validate its effectiveness, experiments were conducted on two major bearing fault datasets. The results demonstrate that the proposed E-DANNMK model achieves an average diagnostic accuracy of 94.21%, outperforming mainstream domain adaptation methods—including CDAN, CORAL, DANN, CNN-Transformer, DMT and DANN-MK—by a margin ranging from 3.12% to 7.15%. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault Tolerant Control in Mechanical System)
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20 pages, 40237 KB  
Article
Bearing Fault Diagnosis Method Based on Multi-Source Information Fusion with Physical Prior Knowledge
by Yuxin Lu, Siyu Shao, Wenxiu Zheng, Xinyu Yang, Kaizhe Jiao, Jun Hu and Bohui Zhang
Machines 2026, 14(1), 67; https://doi.org/10.3390/machines14010067 - 5 Jan 2026
Viewed by 638
Abstract
The working conditions of bearings, as a key component in electromechanical systems, are becoming increasingly complex with the rapid development of current intelligent manufacturing technology. Therefore, it is difficult to accurately identify the abnormal operating state of the bearing through a single signal. [...] Read more.
The working conditions of bearings, as a key component in electromechanical systems, are becoming increasingly complex with the rapid development of current intelligent manufacturing technology. Therefore, it is difficult to accurately identify the abnormal operating state of the bearing through a single signal. In addition, data-based bearing fault diagnosis methods insufficiently utilize bearing prior knowledge under complex working conditions. To address the above issues, this paper proposes a bearing fault diagnosis method based on multi-source information fusion with physical prior knowledge (MSIF-PPK). An information fusion module and a physical embedding module are designed: the former module fuses frequency-domain, time–frequency-domain, and working condition information through an attention mechanism, while the latter one embeds physical working condition data and features. The feasibility and the effectiveness of the modules are verified through comparative experiments and ablation experiments using the Southeast University (SEU) Bearing Dataset, the Mehran University of Engineering and Technology (MUET) Induction Motor Bearing Vibration Dataset, and the Harbin Institute of Technology (HIT) Aeroengine Bearing Dataset. Experimental results show that this method is feasible, reliable, and interpretable for bearing fault diagnosis under complex working conditions. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault Tolerant Control in Mechanical System)
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21 pages, 4491 KB  
Article
An Energy Management Strategy for FCHEVs Using Deep Reinforcement Learning with Thermal Runaway Fault Diagnosis Considering the Thermal Effects and Durability
by Yongqiang Wang, Fazhan Tao, Longlong Zhu, Nan Wang and Zhumu Fu
Machines 2025, 13(10), 962; https://doi.org/10.3390/machines13100962 - 18 Oct 2025
Cited by 1 | Viewed by 961
Abstract
Temperature control plays a critical role in mitigating the lifespan degradation mechanisms and ensuring thermal safety of lithium-ion batteries (LIBs) and proton exchange membrane fuel cells (PEMFCs). However, current energy management strategies (EMS) for fuel cell hybrid electric vehicles (FCHEVs) generally lack comprehensive [...] Read more.
Temperature control plays a critical role in mitigating the lifespan degradation mechanisms and ensuring thermal safety of lithium-ion batteries (LIBs) and proton exchange membrane fuel cells (PEMFCs). However, current energy management strategies (EMS) for fuel cell hybrid electric vehicles (FCHEVs) generally lack comprehensive thermal effect modeling and thermal runaway fault diagnosis, leading to irreversible aging and thermal runaway risks for LIBs and PEMFCs stacks under complex operating conditions. To address this challenge, this paper proposes a thermo-electrical co-optimization EMS incorporating thermal runaway fault diagnosis actuators, with the following innovations: firstly, a dual-layer framework integrates a temperature fault diagnosis-based penalty into the EMS and a real-time power regulator to suppress heat generation and constrain LIBs/PEMFCs output, achieving hierarchical thermal management and improved safety; secondly, the distributional soft actor–critic (DSAC)-based EMS incorporates energy consumption, state-of-health (SoH) degradation, and temperature fault diagnosis-based constraints into a composite penalty function, which regularizes the reward shaping and guides the policy toward efficient and safe operation; finally, a thermal safe constriction controller (TSCC) is designed to continuously monitor the temperature of power sources and automatically activate when temperatures exceed the optimal operating range. It intelligently identifies optimized actions that not only meet target power demands but also comply with safety constraints. Simulation results demonstrate that compared to DDPG, TD3, and SAC baseline strategies, DSAC-EMS achieves maximum reductions of 39.91% in energy consumption and 29.38% in SoH degradation. With the TSCC implementation, enhanced thermal safety is achieved, while the maximum energy-saving improvement reaches 25.29% and the maximum reduction in SoH degradation attains 20.32%. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault Tolerant Control in Mechanical System)
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18 pages, 3548 KB  
Article
A Fault Diagnosis Framework for Waterjet Propulsion Pump Based on Supervised Autoencoder and Large Language Model
by Zhihao Liu, Haisong Xiao, Tong Zhang and Gangqiang Li
Machines 2025, 13(8), 698; https://doi.org/10.3390/machines13080698 - 7 Aug 2025
Cited by 3 | Viewed by 890
Abstract
The ship waterjet propulsion system is a crucial power unit for high-performance vessels, and the operational state of its core component, the waterjet pump, is directly related to navigation safety and mission reliability. To enhance the intelligence and accuracy of pump fault diagnosis, [...] Read more.
The ship waterjet propulsion system is a crucial power unit for high-performance vessels, and the operational state of its core component, the waterjet pump, is directly related to navigation safety and mission reliability. To enhance the intelligence and accuracy of pump fault diagnosis, this paper proposes a novel diagnostic framework that integrates a supervised autoencoder (SAE) with a large language model (LLM). This framework first employs an SAE to perform task-oriented feature learning on raw vibration signals collected from the pump’s guide vane casing. By jointly optimizing reconstruction and classification losses, the SAE extracts deep features that both represent the original signal information and exhibit high discriminability for different fault classes. Subsequently, the extracted feature vectors are converted into text sequences and fed into an LLM. Leveraging the powerful sequential information processing and generalization capabilities of LLM, end-to-end fault classification is achieved through parameter-efficient fine-tuning. This approach aims to avoid the traditional dependence on manually extracted time-domain and frequency-domain features, instead guiding the feature extraction process via supervised learning to make it more task-specific. To validate the effectiveness of the proposed method, we compare it with a baseline approach that uses manually extracted features. In two experimental scenarios, direct diagnosis with full data and transfer diagnosis under limited-data, cross-condition settings, the proposed method significantly outperforms the baseline in diagnostic accuracy. It demonstrates excellent performance in automated feature extraction, diagnostic precision, and small-sample data adaptability, offering new insights for the application of large-model techniques in critical equipment health management. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault Tolerant Control in Mechanical System)
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23 pages, 3087 KB  
Article
MCMBAN: A Masked and Cascaded Multi-Branch Attention Network for Bearing Fault Diagnosis
by Peng Chen, Haopeng Liang and Alaeldden Abduelhadi
Machines 2025, 13(8), 685; https://doi.org/10.3390/machines13080685 - 4 Aug 2025
Viewed by 778
Abstract
In recent years, deep learning methods have made breakthroughs in the field of rotating equipment fault diagnosis, thanks to their powerful data analysis capabilities. However, the vibration signals usually incorporate fault features and background noise, and these features may be scattered over multiple [...] Read more.
In recent years, deep learning methods have made breakthroughs in the field of rotating equipment fault diagnosis, thanks to their powerful data analysis capabilities. However, the vibration signals usually incorporate fault features and background noise, and these features may be scattered over multiple frequency levels, which increases the complexity of extracting important information from them. To address this problem, this paper proposes a Masked and Cascaded Multi-Branch Attention Network (MCMBAN), which combines the Noise Mask Filter Block (NMFB) with the Multi-Branch Cascade Attention Block (MBCAB), and significantly improves the noise immunity of the fault diagnostic model and the efficiency of fault feature extraction. NMFB novelly combines a wide convolutional layer and a top k neighbor self-attention masking mechanism, so as to efficiently filter unnecessary high-frequency noise in the vibration signal. On the other hand, MBCAB strengthens the interaction between different layers by cascading the convolutional layers of different scales, thus improving the recognition of periodic fault signals and greatly enhancing the diagnosis accuracy of the model when processing complex signals. Finally, the time–frequency analysis technique is employed to explore the internal mechanisms of the model in depth, aiming to validate the effectiveness of NMFB and MBCAB in fault feature recognition and to improve the feature interpretability of the proposed modes in fault diagnosis applications. We validate the superior performance of the network model in dealing with high-noise backgrounds by testing it on a standard bearing dataset from Case Western Reserve University and a self-constructed composite bearing fault dataset, and the experimental results show that its performance exceeded six of the top current fault diagnosis techniques. Full article
(This article belongs to the Special Issue Fault Diagnosis and Fault Tolerant Control in Mechanical System)
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