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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 14703

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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

School of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Interests: health monitoring; intelligent identification; transfer learning; deep learning
Special Issues, Collections and Topics in MDPI journals

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 (13 papers)

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Research

23 pages, 5270 KB

Open AccessArticle

Spatio-Temporal Joint Network for Coupler Anomaly Detection Under Complex Working Conditions Utilizing Multi-Source Sensors

by Zhirong Zhao, Zhentian Jiang, Qian Xiao, Long Zhang and Jinbo Wang

Sensors 2026, 26(9), 2661; https://doi.org/10.3390/s26092661 - 24 Apr 2026

Cited by 1 | Viewed by 736

Abstract

Owing to the intricate mechanical coupling characteristics and the considerable difficulty in extracting synergistic spatio-temporal features from high-dimensional sensor data under fluctuating alternating loads, this study proposes a robust anomaly detection framework that combines Normalized Mutual Information (NMI) and Spatio-Temporal Graph Neural Networks (STGNN). First, NMI is utilized to quantify the nonlinear physical coupling intensity among multi-source sensors, thereby filtering out weakly correlated noise and reconstructing the spatial topological structure of the coupler system. Subsequently, a deep learning architecture incorporating Graph Convolutional Networks (GCN), Gated Recurrent Units (GRU), and one-dimensional convolutional residual connections is developed to capture the dynamic evolutionary characteristics of equipment states across both spatial interactions and temporal sequences. Finally, based on the model’s health-state predictions, a moving average algorithm is introduced to smooth the residual sequences, and an anomaly early-warning baseline is established in conjunction with the 3σ criterion. Experimental validation conducted using field service data from heavy-haul trains demonstrates that, compared to conventional serial CNN and Long Short-Term Memory (LSTM) models, the proposed method exhibits superior fitting performance and robustness against noise, effectively reducing the false alarm rate within normal working intervals. In a real-world case study, the method successfully identified variations in spatial linkage features induced by local damage and triggered timely alerts. Notably, the spatial alarm nodes were highly consistent with the fatigue crack initiation sites identified through on-site magnetic particle inspection. This study provides a viable data-driven analytical framework for the condition monitoring and anomaly identification of critical load-bearing components in heavy-haul trains. Full article

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

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27 pages, 4041 KB

Open AccessFeature PaperArticle

Fault Diagnosis Method for Mechanical Components Fusing RPM, DAM-IResNet, and Transfer Learning

by Xingwei Ge, Ziyang Chen, Yachao Cao, Zhe Wu and Qi Li

Sensors 2026, 26(7), 2162; https://doi.org/10.3390/s26072162 - 31 Mar 2026

Viewed by 540

Abstract

This paper proposes a novel fault diagnosis method that integrates a Relative Position Matrix (RPM), a Downsampling Attention Module (DAM), an Improved Residual Network (IResNet), and transfer learning to address the challenges of scarce fault data and poor generalization under variable working conditions. The RPM converts 1D vibration signals into 2D images to enhance feature representation. The DAM achieves lossless feature compression and selection via Haar wavelet downsampling and convolutional attention. An IResNet then performs deep feature learning and classification. A transfer learning strategy further enables effective knowledge adaptation from data-rich source domains to data-scarce target domains, significantly improving performance in cross-condition and small-sample scenarios. Experiments on multiple bearing and gear datasets demonstrate that the proposed method achieves over 99.5% accuracy, with 100% in key transfer tasks, outperforming existing state-of-the-art approaches. The main contributions of this work include the unified RPM-DAM-IResNet framework, a targeted small-sample transfer strategy, and comprehensive validation of its superior accuracy and robustness. Full article

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

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18 pages, 5105 KB

Open AccessArticle

Lightweight Visual Localization of Steel Surface Defects for Autonomous Inspection Robots Based on Improved YOLOv10n

by Jinwu Tong, Xin Zhang, Xinyun Lu, Han Cao, Lengtao Yao and Bingbing Gao

Sensors 2026, 26(7), 2132; https://doi.org/10.3390/s26072132 - 30 Mar 2026

Viewed by 618

Abstract

To address the challenges of steel surface defect detection—characterized by fine-grained textures, substantial scale variations, and complex background interference—conventional lightweight detectors often struggle to balance real-time navigation requirements with high-precision spatial localization on mobile inspection platforms. In this work, we propose KDM-YOLO, a lightweight visual localization and detection method built upon YOLOv10n, designed to provide an efficient perception engine for autonomous inspection robots. The proposed approach enhances the baseline through three key perspectives: feature extraction, context modeling, and multi-scale fusion. Specifically, KWConv is introduced to strengthen the representation of fine-grained texture and edge cues; C2f-DRB is employed to enlarge the effective receptive field and improve long-range dependency perception to reduce missed detections; and a multi-scale attention fusion (MSAF) module is inserted before the detection head to adaptively integrate spatial details with semantic context while suppressing redundant background responses. Ablation studies confirm that each module contributes to performance gains, and their combination yields the best overall results. Comparative experiments further demonstrate that KDM-YOLO significantly improves detection performance while retaining a compact model size and high inference speed. Compared with the YOLOv10n baseline, Precision, Recall and mAP@50 are increased to 91.0%, 93.9%, and 95.4%, respectively, with a parameter count of 3.29 M and an inference speed of 155.6 f/s. These results indicate that KDM-YOLO achieves an ideal balance between the accuracy and computational efficiency required for embedded navigation platforms, providing an effective solution for online autonomous inspection and real-time localization of steel surface defects. Full article

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

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15 pages, 5485 KB

Open AccessArticle

DC Series Arc Fault Detection in Electric Vehicle Charging Systems Using a Temporal Convolution and Sparse Transformer Network

by Kai Yang, Shun Zhang, Rongyuan Lin, Ran Tu, Xuejin Zhou and Rencheng Zhang

Sensors 2026, 26(6), 1897; https://doi.org/10.3390/s26061897 - 17 Mar 2026

Viewed by 534

Abstract

In electric vehicle (EV) charging systems, DC series arc faults, due to their high concealment and severe hazard, have become one of the important causes of electric vehicle fire accidents. An improved hybrid arc fault model of a charging system was established in Simulink for preliminary study. The results show that the high-frequency noise generated by arc faults affects the output voltage quality of the charger, and this noise is conducted to the battery voltage. Arc faults in a real electric vehicle charging experimental platform were further investigated, where it was found that, during arc fault events, the charging system provides no alarm indication, and the current signals exhibit significant large-amplitude random disturbances and nonlinear fluctuations. Moreover, under normal conditions during vehicle charging startup and the pre-charge stage, the current waveforms also present high-pulse spike characteristics similar to arc faults. Finally, a carefully designed deep neural network-based arc fault detection algorithm, Arc_TCNsformer, is proposed. The current signal samples are directly input into the network model without manual feature selection or extraction, enabling end-to-end fault recognition. By integrating a temporal convolutional network for multi-scale local feature extraction with a sparse Transformer for contextual information aggregation, the proposed method achieves strong robustness under complex charging noise environments. Experimental results demonstrate that the algorithm not only provides high detection accuracy but also maintains reliable real-time performance when deployed on embedded edge computing platforms. Full article

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

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30 pages, 14380 KB

Open AccessArticle

An Explainable Intelligent Fault Diagnosis for Rotating Machinery via Multi-Source Information Fusion Under Noisy Environments and Small Sample Conditions

by Gaolei Mao, Jinhua Wang and Yali Sun

Sensors 2026, 26(5), 1713; https://doi.org/10.3390/s26051713 - 8 Mar 2026

Viewed by 640

Abstract

In modern industrial systems, the fault diagnosis of rotating machinery is crucial for ensuring safe equipment operation. However, practical fault data are often contaminated by noise, and the scarcity of samples across fault conditions makes effective feature extraction challenging. Moreover, single-sensor measurements provide limited and incomplete information, further degrading the accuracy and reliability of diagnostic models. To address these challenges, this paper proposes an explainable intelligent fault diagnosis for rotating machinery via multi-source information fusion under noisy environments and small sample conditions. Firstly, a multi-sensor data intelligent fusion module (MSDIFM) is developed. It converts multi-sensor vibration signals into time–frequency maps via continuous wavelet transform (CWT). Pixel-level cross-channel fusion is then performed using a variance-driven dynamic weighting strategy to generate a unified fusion map, adaptively highlighting high information channels. Secondly, a multi-dimensional adaptive asymmetric soft-threshold residual shrinkage block (MASRSB) is proposed to implement differentiated and dynamic threshold control for positive and negative features, enhancing representation and discrimination capabilities. Thirdly, the multi-scale Swin Transformer (MSSwin-T) is designed. This module significantly enhances the model’s feature extraction capability by expanding multi-level receptive fields, strengthening key channel representations, and reinforcing cross-window feature interactions. Finally, to validate the effectiveness of the proposed method, experiments are conducted on both the Case Western Reserve University (CWRU) dataset and the self-created PT890 dataset. Results demonstrate that the proposed method exhibits outstanding diagnostic performance and robustness under noisy conditions and with small sample sizes. Full article

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

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19 pages, 3746 KB

Open AccessArticle

Fault Diagnosis and Classification of Rolling Bearings Using ICEEMDAN–CNN–BiLSTM and Acoustic Emission

by Jinliang Li, Haoran Sheng, Bin Liu and Xuewei Liu

Sensors 2026, 26(2), 507; https://doi.org/10.3390/s26020507 - 12 Jan 2026

Cited by 2 | Viewed by 630

Abstract

Reliable operation of rolling bearings is essential for mechanical systems. Acoustic emission (AE) offers a promising approach for bearing fault detection because of its high-frequency response and strong noise-suppression capability. This study proposes an intelligent diagnostic method that combines an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and a convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture. The method first applies wavelet denoising to AE signals, then uses ICEEMDAN decomposition followed by kurtosis-based screening to extract key fault components and construct feature vectors. Subsequently, a CNN automatically learns deep time–frequency features, and a BiLSTM captures temporal dependencies among these features, enabling end-to-end fault identification. Experiments were conducted on a bearing acoustic emission dataset comprising 15 operating conditions, five fault types, and three rotational speeds; comparative model tests were also performed. Results indicate that ICEEMDAN effectively suppresses mode mixing (average mixing rate 6.08%), and the proposed model attained an average test-set recognition accuracy of 98.00%, significantly outperforming comparative models. Moreover, the model maintained 96.67% accuracy on an independent validation set, demonstrating strong generalization and practical application potential. Full article

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

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30 pages, 8453 KB

Open AccessArticle

PBZGNet: A Novel Defect Detection Network for Substation Equipment Based on Gradual Parallel Branch Architecture

by Mintao Hu, Yang Zhuang, Jiahao Wang, Yaoyi Hu, Desheng Sun, Dawei Xu and Yongjie Zhai

Sensors 2026, 26(1), 300; https://doi.org/10.3390/s26010300 - 2 Jan 2026

Viewed by 798

Abstract

As power systems expand and grow smarter, the safe and steady operation of substation equipment has become a prerequisite for grid reliability. In cluttered substation scenes, however, existing deep learning detectors still struggle with small targets, multi-scale feature fusion, and precise localization. To overcome these limitations, we introduce PBZGNet, a defect-detection network that couples a gradual parallel-branch backbone, a zoom-fusion neck, and a global channel-recalibration module. First, BiCoreNet is embedded in the feature extractor: dual-core parallel paths, reversible residual links, and channel recalibration cooperate to mine fault-sensitive cues. Second, cross-scale ZFusion and Concat-CBFuse are dynamically merged so that no scale loses information; a hierarchical composite feature pyramid is then formed, strengthening the representation of both complex objects and tiny flaws. Third, an attention-guided decoupled detection head (ADHead) refines responses to obscured and minute defect patterns. Finally, within the Generalized Focal Loss framework, a quality rating scheme suppresses background interference while distribution regression sharpens the localization of small targets. Across all scales, PBZGNet clearly outperforms YOLOv11. Its lightweight variant, PBZGNet-n, attains 83.9% mAP@50 with only 2.91 M parameters and 7.7 GFLOPs—9.3% above YOLOv11-n. The full PBZGNet surpasses the current best substation model, YOLO-SD, by 7.3% mAP@50, setting a new state of the art (SOTA). Full article

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

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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

Cited by 2 | Viewed by 711

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

Cited by 1 | Viewed by 795

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 646

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 2 | Viewed by 1575

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 6 | Viewed by 3130

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 8 | Viewed by 2234

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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