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Deep Learning Based Intelligent Fault Diagnosis

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

Deadline for manuscript submissions: 31 July 2026 | Viewed by 6316

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Special Issue Editors

Dr. Long Zhang

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Guest Editor

School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Interests: signal processing and intelligent fault diagnosis of complex mechanical systems
Special Issues, Collections and Topics in MDPI journals

Dr. Jiayang Liu

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Guest Editor

School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Interests: fault diagnosis; deep learning; transfer learning; anomaly detection

Dr. Xiaoli Zhao

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Guest Editor

School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
Interests: diagnostics, prognostics and health management (PHM) for electromechanical and hydraulic equipment; artificial intelligence and signal processing; digital twins
Special Issues, Collections and Topics in MDPI journals

Dr. Zhenghong Wu

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Guest Editor

Xi’an Modern Control Technology Research Institute, Xi’an 710000, China
Interests: health monitoring; intelligent identification; transfer learning; deep learning

Special Issue Information

Dear Colleagues,

The rapid development of deep learning has significantly transformed various fields, including fault diagnosis in complex systems. Intelligent fault diagnosis, leveraging deep learning techniques, offers unprecedented opportunities to improve the reliability, safety, and efficiency of machinery and equipment. By harnessing deep learning, researchers can uncover intricate patterns, enhance fault identification accuracy, and adapt to diverse operational conditions, addressing challenges such as non-stationary signals, data scarcity, and cross-domain variability.

We are pleased to invite you to contribute to this Special Issue titled “Deep Learning Based Intelligent Fault Diagnosis” to share your innovative research and insights into this vital and evolving field.

This Special Issue aims to highlight recent advances in combining deep learning with sensing technologies, multi-sensor information fusion, and diagnostic techniques while emphasizing innovative solutions for real-world engineering problems and fostering multidisciplinary approaches to enhance system diagnostics. The scope of this Special Issue encompasses theoretical advances, algorithm development, and practical applications, including (but not limited to) the following topics of interest:

Novel deep learning architectures for fault diagnosis;
Explainable AI techniques in fault diagnosis;
Cross-domain fault diagnosis;
Real-time fault detection and prediction;
Data augmentation and imbalance handling in deep learning for fault diagnosis;
Case studies of deep learning-based fault diagnosis in industrial applications (e.g., railway vehicles, wind turbines, aerospace).

Dr. Long Zhang
Dr. Jiayang Liu
Dr. Xiaoli Zhao
Dr. Zhenghong Wu
Guest Editors

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Keywords

fault diagnosis
deep learning
transfer learning
domain generalization
feature extraction
condition monitoring
fault prognostics
anomaly detection
condition assessment

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Published Papers (6 papers)

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Research

17 pages, 2225 KB

Open AccessArticle

A Knowledge-Guide Data-Driven Model with Selective Wavelet Kernel Fusion Neural Network for Gearbox Intelligent Fault Diagnosis

by Nan Zhuang, Zhaogang Ren, Dongyao Yang, Xu Tian and Yingwu Wang

Sensors 2025, 25(24), 7656; https://doi.org/10.3390/s25247656 - 17 Dec 2025

Viewed by 192

Abstract

The gearbox is a critical component in modern industrial systems, directly determining the operational reliability of machinery. Therefore, effective fault diagnosis is essential to ensure its proper functioning. Modern diagnostic approaches often employ accelerometers to monitor vibration signals and apply data-driven techniques for fault identification, achieving considerable success. However, deep learning-based methods still face limitations due to their “black-box” nature and lack of interpretability. To address these issues, this paper proposes a knowledge-guided selective wavelet kernel fusion neural network. By integrating diagnostic domain knowledge into data-driven modeling, the proposed method enhances both the interpretability and diagnostic performance of intelligent fault diagnosis systems. First, a multi-kernel convolutional module is designed based on domain knowledge and embedded into a Modern Temporal Convolutional Network. Then, an attention-based selective wavelet kernel fusion strategy is introduced to adaptively fuse kernels according to the distribution of different datasets. Finally, the effectiveness of the proposed method is validated on two public datasets. Experimental results demonstrate that the approach not only provides prior interpretability, which overcoming the black-box limitation of deep learning, but also further improves diagnostic accuracy. Full article

(This article belongs to the Special Issue Deep Learning Based Intelligent Fault Diagnosis)

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22 pages, 2694 KB

Open AccessArticle

Research on Fault Diagnosis of Mechanical Bearings Based on Transfer Learning

by Xinjian Gao, Yizhi Zhang, Enzhi Dong, Zhifeng You, Liang Wen and Zhonghua Cheng

Sensors 2025, 25(24), 7446; https://doi.org/10.3390/s25247446 - 7 Dec 2025

Viewed by 261

Abstract

Intelligent fault diagnosis is a set of methods for the health monitoring of mechanical bearings. To address the problem of insufficient applicability of diagnostic models due to differences in the domain distribution between laboratory data and actual working conditions, this study constructs a complete transfer learning diagnostic system. Firstly, the Hilbert transform technique was introduced to extract time-domain and frequency-domain features, as well as periodic correlations and other indicators; then, three models, i.e., transfer learning (TL), gradient boosting machine (GBM), and random forest (RF), were used to classify the data and compare their accuracy. It was found that TL had the highest accuracy in testing, with an F1 score of 0.9631. In the transfer task of the target domain samples, compared with the direct application of the source domain model with a classification accuracy of 70.3%, the transfer learning method achieved a classification accuracy of 97.6%, and the transfer gain increased by 27.3 percentage points, proving the superiority of the model constructed in this paper. Finally, SHapley Additive exPlanations (SHAP) was used to provide a detailed explanation of the transfer learning model, and the basis for model decision making was revealed through feature importance analysis. Full article

(This article belongs to the Special Issue Deep Learning Based Intelligent Fault Diagnosis)

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29 pages, 3619 KB

Open AccessArticle

Bearing Fault Diagnosis via FMD with Parameters Optimized by an Improved Crested Porcupine Optimizer

by Ping Pan, Hao Liu, Bing Lei and Xiaohong Tang

Sensors 2025, 25(23), 7339; https://doi.org/10.3390/s25237339 - 2 Dec 2025

Viewed by 248

Abstract

Feature Mode Decomposition (FMD) can effectively extract bearing fault features even in the case of strong interference noise by means of adaptive finite impulse response filter banks along with correlated kurtosis. Nevertheless, the filter length L and the number of decomposition modes K need to be predefined carefully in a manual way. Otherwise, mismatched parameters could lead to redundant components or even missed detection of fault information. To mitigate the reliance on manual parameter setting, recent studies have introduced optimization algorithms such as the Whale Optimization Algorithm and the Crested Porcupine Optimizer to find the optimal parameters for FMD. However, such methods usually suffer from the dilemma of easily premature convergence in global search and long-time consumption in local fine adjustment, rendering them with difficulty in meeting the requirements of real-time and accurate diagnosis. Therefore, this paper proposes an improved Crested Porcupine Optimizer (ICPO), which can dynamically balance global and local exploitation. Furthermore, a bearing fault diagnosis method named ICPO-FMD is constructed, wherein the optimal parameter combination of K and L obtained using ICPO is provided to FMD in order to decompose bearing signals into a family of intrinsic mode functions (IMFs), and then fault sensitive components are extracted according to the proposed IMF screening principle. Finally, a reconstructed signal is obtained, followed by an envelope demodulation analysis. Experiments on simulation, laboratory and engineering signals demonstrate that the proposed method can accurately extract the fault characteristic frequency and its harmonics. Full article

(This article belongs to the Special Issue Deep Learning Based Intelligent Fault Diagnosis)

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31 pages, 5280 KB

Open AccessArticle

Attention Mechanism-Based Feature Fusion and Degradation State Classification for Rolling Bearing Performance Assessment

by Teng Zhan, Wentao Chen, Congchang Xu, Luoxing Li and Xiaoxi Ding

Sensors 2025, 25(16), 4951; https://doi.org/10.3390/s25164951 - 10 Aug 2025

Cited by 1 | Viewed by 1020

Abstract

Rolling bearing failure poses significant risks to mechanical system integrity, potentially leading to catastrophic safety incidents. Current challenges in performance degradation assessment include complex structural characteristics, suboptimal feature selection, and inadequate health index characterization. This study proposes a novel attention mechanism-based feature fusion method for accurate bearing performance assessment. First, we construct a multidimensional feature set encompassing time domain, frequency domain, and time–frequency domain characteristics. A two-stage sensitive feature selection strategy is developed, combining intersection-based primary selection with clustering-based re-selection to eliminate redundancy while preserving correlation, monotonicity, and robustness. Subsequently, an attention mechanism-driven fusion model adaptively weights selected features to generate high-performance health indicators. Experimental validation demonstrates the proposed method’s superiority in degradation characterization through two case studies. The intersection clustering strategy achieves 32% redundancy reduction compared to conventional methods, while the attention-based fusion improves health indicator consistency by 18.7% over principal component analysis. This approach provides an effective solution for equipment health monitoring and early fault warning in industrial applications. Full article

(This article belongs to the Special Issue Deep Learning Based Intelligent Fault Diagnosis)

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24 pages, 10080 KB

Open AccessArticle

Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis

by Jia Xu, Yan Wang, Renyi Xu, Hailin Wang and Xinzhi Zhou

Sensors 2025, 25(10), 3019; https://doi.org/10.3390/s25103019 - 10 May 2025

Cited by 2 | Viewed by 2342

Abstract

In rolling bearing fault diagnosis, when an unknown fault is present, the Closed-Set Recognition (CSR) method tends to misclassify it as a known fault. To address this issue, an Open-Set Recognition (OSR) framework is proposed for rolling bearing fault diagnosis in this study. The framework is built upon a serial multi-scale convolutional prototype learning (SMCPL) network, enhanced with an efficient channel attention (ECA) mechanism to extract the most critical fault features. The extracted features are fed into the Density Peak Clustering (DPC) module, which identifies known and unknown classes based on the computed local densities and relative distances. Finally, validation is performed on the Case Western Reserve University (CWRU) dataset, the Xi’an Jiaotong University rolling bearing accelerated life test dataset (XJTU-SY), and the Paderborn University bearing dataset (PU), Germany, and the framework is comprehensively evaluated in terms of several evaluation metrics, such as normalization accuracy and feature visualization. The experimental results show that SMCPL-ECA-DPC outperforms the comparative methods of SMCPL, CPL, ANEDL, CNN, and OpenMax and has high diagnostic performance in the identification of unknown faults. Full article

(This article belongs to the Special Issue Deep Learning Based Intelligent Fault Diagnosis)

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16 pages, 6997 KB

Open AccessArticle

Efficient Gearbox Fault Diagnosis Based on Improved Multi-Scale CNN with Lightweight Convolutional Attention

by Bin Yuan, Yaoqi Li and Suifan Chen

Sensors 2025, 25(9), 2636; https://doi.org/10.3390/s25092636 - 22 Apr 2025

Cited by 4 | Viewed by 1650

Abstract

As a core transmission component of modern industrial equipment, the operation status of the gearbox has a significant impact on the reliability and service life of major machinery. In this paper, we propose an intelligent diagnosis framework based on Empirical Mode Decomposition and multimodal feature co-optimization and innovatively construct a fault diagnosis model by fusing a multi-scale convolutional neural network and a lightweight convolutional attention model. The framework extracts the multi-band features of vibration signals through the improved multi-scale convolutional neural network, which significantly enhances adaptability to complex working conditions (variable rotational speed, strong noise); at the same time, the lightweight convolutional attention mechanism is used to replace the multi-attention of the traditional Transformer, which greatly reduces computational complexity while guaranteeing accuracy and realizes highly efficient, lightweight local–global feature modeling. The lightweight convolutional attention is adaptively captured by the dynamic convolutional kernel generation strategy to adaptively capture local features in the time domain, and combined with grouped convolution to enhance the computational efficiency further; in addition, parameterized revised linear units are introduced to retain fault-sensitive negative information, which enhances the model’s ability to detect weak faults. The experimental findings demonstrate that the proposed model achieves an accuracy greater than 98.9%, highlighting its exceptional diagnostic accuracy and robustness. Moreover, compared to other fault diagnosis methods, the model exhibits superior performance under complex working conditions. Full article

(This article belongs to the Special Issue Deep Learning Based Intelligent Fault Diagnosis)

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