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Machines
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Recent Advances in Condition Monitoring and Fault Diagnosis of Rotating...
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Recent Advances in Condition Monitoring and Fault Diagnosis of Rotating Machinery

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1471

Editor

Dr. Tianci Zhang

E-Mail Website
Guest Editor
State Key Laboratory of Precision Manufacturing for Extreme Service Performance, Central South University, Changsha 410083, China
Interests: mechanical signal processing; intelligent fault diagnosis; machine learning and deep learning

Special Issue Information

Dear Colleagues,

Rotating machinery is the backbone of critical industrial systems, including energy generation, manufacturing, transportation, and aerospace. Unexpected failures can lead to costly downtime, safety hazards, and environmental impacts. The evolution of sensing technologies, data analytics, and artificial intelligence has revolutionized condition monitoring (CM) and fault diagnosis (FD), yet challenges persist in accuracy, adaptability, and real-time deployment. This Special Issue seeks cutting-edge research addressing these challenges. We invite original contributions and reviews focusing on recent innovations in the following areas:

  1. Advanced Sensing and Signal Acquisition: Novel sensors (e.g., MEMS, wireless), embedded systems, and IoT integration.
  2. Intelligent Data Processing: AI/ML-driven diagnosis (deep learning, transfer learning), signal decomposition techniques (e.g., variational modes), and noise-robust feature extraction.
  3. Fault Prognosis and Digital Twins: Predictive maintenance frameworks, remaining useful life (RUL) estimation, and physics-informed digital twins.
  4. Emerging Applications: Real-time monitoring in harsh environments, edge computing, and scalable solutions for Industry 4.0/5.0.

Submissions should demonstrate rigorous validation via case studies, simulations, or experimental data. By compiling state-of-the-art methodologies, this issue aims to enhance the reliability, safety, and efficiency of rotating machinery across industries.

Dr. Tianci Zhang
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-anonymized 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
  • condition monitoring
  • rotating machinery
  • mechanical signal processing
  • deep learning

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Published Papers (2 papers)

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Research

28 pages, 5504 KB  
Article
Multimodal Heterogeneous CNN with Adaptive Modality Fusion for Intelligent Fault Diagnosis of Bearings
by Chang Sun, Chenkun Wang, Shiwei Huang and Tianci Zhang
Machines 2026, 14(8), 875; https://doi.org/10.3390/machines14080875 - 1 Aug 2026
Viewed by 355
Abstract
In industrial equipment fault diagnosis, vibration and acoustic signals are highly complementary yet exhibit significant differences in frequency distribution and noise sensitivity. Traditional multimodal methods generally rely on homogeneous feature extractors and direct feature concatenation, which may fail to capture modality-specific characteristics and [...] Read more.
In industrial equipment fault diagnosis, vibration and acoustic signals are highly complementary yet exhibit significant differences in frequency distribution and noise sensitivity. Traditional multimodal methods generally rely on homogeneous feature extractors and direct feature concatenation, which may fail to capture modality-specific characteristics and introduce irrelevant information during fusion. To address this, we propose a novel multimodal heterogeneous convolutional neural network framework. Specifically, separate 1D CNN branches are designed for vibration and acoustic signals. Their architectural differences are determined by the characteristics of each sensing modality. The vibration branch focuses on extracting high-level discriminative fault features, including impulse responses and modulated components from vibration signals, while the acoustic branch is designed to preserve fragile high-frequency details of acoustic signals. Furthermore, an adaptive cross-attention fusion module is introduced to dynamically model cross-modal dependencies, assigning Softmax-based weights to enhance dominant features and suppress noise. Experiments based on bearing fault experimental data demonstrate that the proposed heterogeneous architecture significantly outperforms traditional homogeneous models. The dynamic weighting mechanism effectively prevents inferior noisy modalities from degrading overall performance, achieving high diagnostic accuracy. Although validated on rolling bearing fault diagnosis, the proposed heterogeneous multimodal framework is not restricted to bearings and can be readily extended to other intelligent condition monitoring tasks involving heterogeneous sensor fusion, such as gearboxes, motors, and other rotating machinery. Full article
(This article belongs to the Special Issue Recent Advances in Condition Monitoring and Fault Diagnosis of Rotating Machinery)
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29 pages, 2784 KB  
Article
Condition-Aware DANN-LSTM for Rolling-Bearing Fault Diagnosis and Remaining Useful Life Prediction Under Operating Condition Shifts
by Yangfeng Ji, Rongfei Xia and Miaojiao Peng
Machines 2026, 14(6), 682; https://doi.org/10.3390/machines14060682 - 11 Jun 2026
Viewed by 440
Abstract
Rolling element bearing monitoring under operating condition shifts remains difficult because fault signatures are transient, fault data are scarce, and degradation trends may depend on load and speed. This study evaluates a condition-aware DANN-LSTM framework for joint fault diagnosis and RUL prediction. A [...] Read more.
Rolling element bearing monitoring under operating condition shifts remains difficult because fault signatures are transient, fault data are scarce, and degradation trends may depend on load and speed. This study evaluates a condition-aware DANN-LSTM framework for joint fault diagnosis and RUL prediction. A one-dimensional CNN extracts vibration features, a gradient reversal branch aligns condition-related distributions for fault classification, and an LSTM models chronological degradation features without direct adversarial regularization. The model jointly optimizes classification, condition-discrimination, and RUL losses. Experiments on public bearing datasets show high class-wise identification rates, a validation accuracy of 0.989, and an RUL RMSE of 7.9. Controlled ablation indicates that moderate condition alignment improves transfer classification while preserving useful degradation ordering for RUL prediction. The framework offers a practical data-driven baseline for bearing condition monitoring under controlled condition shifts. Full article
(This article belongs to the Special Issue Recent Advances in Condition Monitoring and Fault Diagnosis of Rotating Machinery)
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