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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 January 2026 | Viewed by 612

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 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.

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 (2 papers)

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Research

18 pages, 3548 KiB  
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
Viewed by 208
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 KiB  
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 218
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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