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Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches

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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: 30 September 2026 | Viewed by 6874

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Special Issue Editors

Prof. Dr. Jong-Myon Kim

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Guest Editor

Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
Interests: fault diagnosis; prognosis; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Dr. Zahoor Ahmad

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Guest Editor

Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Republic of Korea
Interests: fault detection and diagnosis; signal processing; multiscale signal analysis; sensor fusion; signal to image conversion and analysis; artificial intelligence; explainable machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bearings are fundamental components in rotating machinery, supporting motion and reducing friction between moving parts. They are widely used in industries such as manufacturing, transportation, and aerospace. As machinery becomes more advanced and operates under increasingly demanding and harsh conditions, bearings are subjected to high loads, speeds, and prolonged operation. These factors contribute to wear and the development of incipient faults, which, if undetected, can lead to severe consequences, including machine failure, downtime, economic loss, and safety hazards. Therefore, accurate and early diagnosis of bearing faults is essential for improving equipment reliability, optimizing maintenance, and ensuring operational safety.

This Special Issue focuses on recent advances in bearing fault diagnosis using both single-sensor techniques and multi-sensor fusion approaches. Single sensor systems are favored for their simplicity and low cost, while sensor fusion strategies enhance fault detection and classification by integrating data from multiple sensing modalities such as vibration, acoustic emission, and current signals, etc.

This Special Issue invites original research articles and reviews that explore novel methodologies, signal processing techniques, machine learning models, deep learning architectures, and practical applications related to both single-sensor and sensor fusion-based bearing fault diagnosis. Furthermore, research concerning recent advances in sensor fault diagnosis is also encouraged.

Prof. Dr. Jong-Myon Kim
Dr. Zahoor Ahmad
Guest Editors

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Keywords

bearing fault diagnosis
single sensor techniques
sensor fusion
condition monitoring
machine learning

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Published Papers (9 papers)

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Research

13 pages, 4462 KB

Open AccessArticle

A Lightweight 1D-CNN-Transformer for Bearing Fault Diagnosis Under Limited Data and AWGN Interference

by Yifan Guo, Yijie Zhi, Renyi Qi and Ming Cai

Sensors 2026, 26(9), 2574; https://doi.org/10.3390/s26092574 - 22 Apr 2026

Viewed by 284

Abstract

Intelligent bearing fault diagnosis is essential for maintaining the reliability of rotating machinery. However, deploying deep learning models in industrial environments is often constrained by a lack of labeled data, environmental noise, and strict hardware limits. To address these connected challenges, this paper proposes 1D-CNN-Trans, a flexible and resource-efficient hybrid framework. Designed for supervised diagnosis with restricted data, the configurable model combines a compact one-dimensional convolutional neural network (1D-CNN) for local feature extraction, a Transformer encoder for capturing long-range temporal dependencies, and an optional squeeze-and-excitation (SE) module for channel recalibration under favorable conditions. The method is evaluated on two standard mechanical benchmarks under limited sample conditions, controlled additive white Gaussian noise (AWGN), and dynamic non-stationary interference. Experimental results indicate that 1D-CNN-Trans shows improved robustness under interference compared to selected baselines, notably improving accuracy against a standard CNN backbone. Furthermore, findings indicate that while the Transformer ensures noise robustness, channel recalibration (via SE) introduces optimization instability under extreme sparsity and noise. Consequently, we reposition the architecture as a configurable framework where recalibration is conditionally activated. Finally, theoretical complexity analysis is provided to validate the model’s low computational burden, indicating its general feasibility for resource-constrained scenarios. Full article

(This article belongs to the Special Issue Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches)

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36 pages, 4059 KB

Open AccessArticle

Leakage-Resistant Multi-Sensor Bearing Fault Diagnosis via Adaptive Time-Frequency Graph Learning and Sensor Reliability-Aware Fusion

by Yu Sun, Yihang Qin, Wenhao Chen, Wenhui Zhao and Haoran Sun

Sensors 2026, 26(8), 2484; https://doi.org/10.3390/s26082484 - 17 Apr 2026

Viewed by 168

Abstract

Reliable multi-sensor bearing fault diagnosis is challenged by temporal leakage caused by window-level random splitting, limited modeling of cross-sensor dependencies, and inadequate integration of raw temporal dynamics with time-frequency representations. To address these issues, this study proposes a leakage-resistant multi-sensor diagnosis framework that combines a partition-before-windowing evaluation protocol with adaptive time-frequency graph learning and reliability-aware fusion. Continuous vibration records are first divided into disjoint temporal regions with guard intervals and overlap auditing to suppress time-neighbor leakage. The model then extracts complementary features from a raw-signal branch and a dual-resolution log-STFT branch, while adaptive graph learning captures sample-dependent inter-sensor couplings and sensor reliability weighting highlights informative channels. A cross-gated fusion module further integrates temporal and graph-domain representations in a sample-adaptive manner for final classification. Experiments on a reconstructed nine-class benchmark derived from the HUSTbearing dataset show that the proposed method achieves a Macro-Accuracy of 0.973, a Macro-Recall of 0.964, and a Macro-F1 of 0.954, outperforming representative raw-signal and STFT-based baselines under the same leakage-resistant protocol. These results demonstrate that jointly modeling multi-scale time-frequency structure, dynamic sensor relationships, and reliable evaluation yields an effective and interpretable solution for intelligent bearing fault diagnosis under complex operating conditions. Full article

(This article belongs to the Special Issue Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches)

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32 pages, 5547 KB

Open AccessArticle

GMRVGG: A Bearing Fault Diagnosis Method Based on Tri-Modal Image Feature Fusion

by Ao Li, Yuantao Li, Xiaoli Wang and Jiancheng Yin

Sensors 2026, 26(8), 2426; https://doi.org/10.3390/s26082426 - 15 Apr 2026

Viewed by 198

Abstract

Bearings serve as vital components in rotating machinery. Fault diagnosis of bearings constitutes an essential area within mechanical health monitoring. However, most existing methods rely solely on single-modal data or employ a single signal-to-image conversion technique, leading to insufficient information dimensionality and inadequate feature representation, which ultimately limits diagnostic accuracy. To address these challenges, this paper proposes a bearing fault diagnosis method (GADF-MTF-RP-VGG16, GMRVGG) based on tri-modal image feature fusion. Specifically, three image conversion techniques—Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP)—are utilized to first convert 1D vibration signals into 2D images. Subsequently, shallow to deep features are extracted and fused through the VGG16 backbone network. Finally, fault diagnosis is achieved by integrating a fully connected classifier layer. The proposed methodology was comprehensively validated on both the Case Western Reserve University (CWRU) and the University of Ottawa datasets, which were augmented with severe 6 dB Gaussian white noise and 6 dB pink noise to simulate complex industrial environments. Under these harsh conditions, the proposed method achieved superior overall accuracies (up to 96.9% on the CWRU dataset and consistently 95.8% on the Ottawa dataset), significantly surpassing conventional single-modal approaches. This effectively addresses the limitations of insufficient feature dimensionality and inadequate representation, establishing a highly reliable and robust solution for intelligent bearing fault diagnosis. Full article

(This article belongs to the Special Issue Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches)

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21 pages, 4345 KB

Open AccessArticle

Real-World Airborne Sound Analysis for Health Monitoring of Bearings in Railway Vehicles

by Matthias Kreuzer, David Schmidt, Simon Wokusch and Walter Kellermann

Sensors 2026, 26(6), 1947; https://doi.org/10.3390/s26061947 - 20 Mar 2026

Viewed by 349

Abstract

In this paper, the task of detecting bearing faults in railway vehicles during regular operation by analyzing acoustic (airborne sound) data is addressed. To that end, various features are studied, among which the Mel Frequency Cepstral Coefficients (MFCCs) are best suited for detecting bearing faults by analyzing airborne sound. The MFCCs are used to train a Multi-Layer Perceptron (MLP) classifier. The proposed method is evaluated with real-world data for a state-of-the-art commuter railway vehicle in a dedicated measurement campaign. Classification results demonstrate that the chosen MFCC features allow for reliable detection of bearing damages, even for damages that were not included in training. Full article

(This article belongs to the Special Issue Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches)

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21 pages, 3134 KB

Open AccessArticle

An Imbalanced Fault Diagnosis Method Based on Multi-Sensor Selection and Graph Attention Mechanism

by Qiangqiang Xiong, Qiming Shu, Ke Wu, Jun Wu and Jianwen Hu

Sensors 2026, 26(4), 1182; https://doi.org/10.3390/s26041182 - 11 Feb 2026

Viewed by 437

Abstract

Severe diagnostic errors are often caused by the significant imbalance between normal and fault data in bearing datasets. To solve this challenge, a graph attention convolutional neural network based on sensitivity analysis and correlation analysis (SCGAT) is proposed to achieve bearing fault diagnosis under imbalanced-dataset conditions. Firstly, a graph attention convolutional neural network is constructed to effectively extract fault-related features from multi-sensor data. Then, a sensor sensitivity analysis module is built to filter and select effective sensor information. A sensor correlation analysis module is introduced to distinguish the correlation between different sensors, and strongly correlated sensors are merged. Finally, the merged features are input into a classifier for fault diagnosis. The effectiveness of the proposed method is verified on a power transmission simulation experiment platform. The experimental results show that the proposed SCGAT can effectively achieve fault diagnosis under imbalanced data conditions, and exhibits higher diagnostic accuracy and stability compared to other models. Full article

(This article belongs to the Special Issue Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches)

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18 pages, 2688 KB

Open AccessArticle

Rolling Bearing Fault Diagnosis Based on Multi-Source Domain Joint Structure Preservation Transfer with Autoencoder

by Qinglei Jiang, Tielin Shi, Xiuqun Hou, Biqi Miao, Zhaoguang Zhang, Yukun Jin, Zhiwen Wang and Hongdi Zhou

Sensors 2026, 26(1), 222; https://doi.org/10.3390/s26010222 - 29 Dec 2025

Cited by 2 | Viewed by 515

Abstract

Domain adaptation methods have been extensively studied for rolling bearing fault diagnosis under various conditions. However, some existing methods only consider the one-way embedding of original space into a low-dimensional subspace without backward validation, which leads to inaccurate embeddings of data and poor diagnostic performance. In this paper, a rolling bearing fault diagnosis method based on multi-source domain joint structure preservation transfer with autoencoder (MJSPTA) is proposed. Firstly, similar source domains are screened by inter-domain metrics; then, the high-dimensional data of both the source and target domains are projected into a shared subspace with different projection matrices, respectively, during the encoding stage. Finally, the decoding stage reconstructs the low-dimensional data back to the original high-dimensional space to minimize the reconstruction accuracy. In the shared subspace, the difference between source and target domains is reduced through distribution matching and sample weighting. Meanwhile, graph embedding theory is introduced to maximally preserve the local manifold structure of the samples during domain adaptation. Next, label propagation is used to obtain the predicted labels, and a voting mechanism ultimately determines the fault type. The effectiveness and robustness of the method are verified through a series of diagnostic tests. Full article

(This article belongs to the Special Issue Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches)

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26 pages, 5989 KB

Open AccessArticle

A Gradient-Penalized Conditional TimeGAN Combined with Multi-Scale Importance-Aware Network for Fault Diagnosis Under Imbalanced Data

by Ranyang Deng, Dongning Chen, Chengyu Yao, Dongbo Hu, Qinggui Xian and Sheng Zhang

Sensors 2025, 25(22), 6825; https://doi.org/10.3390/s25226825 - 7 Nov 2025

Cited by 1 | Viewed by 966

Abstract

In real-world industrial settings, obtaining class-balanced fault data is often difficult. Imbalanced data across categories can degrade diagnostic accuracy. Time-series Generative Adversarial Network (TimeGAN) is an effective tool for addressing one-dimensional data imbalance; however, when dealing with multiple fault categories, it faces issues such as unstable training processes and uncontrollable generation states. To address this issue, from the perspective of data augmentation and classification, a gradient-penalized Conditional Time-series Generative Adversarial Network with a Multi-Scale Importance-aware Network (CTGAN-MSIN) is proposed in this paper. Firstly, a gradient-penalized Conditional Time-Series Generative Adversarial Network (CTGAN) is designed to alleviate data imbalance by controllably generating high-quality fault samples. Secondly, a Multi-scale Importance-aware Network (MSIN) is constructed for fault classification. The MSIN consists of the Multi-scale Depthwise Separable Residual (MDSR) and Scale Enhanced Local Attention (SELA): the MDSR network can efficiently extract multi-scale features, while the SELA network is capable of screening out the most discriminative scale features from them. Finally, the proposed method is validated using the HUST bearing dataset and the axial piston pump dataset. The results show that under the data imbalance ratio of 15:1, the CTGAN-MSIN achieves diagnostic accuracies of 98.75% and 96.50%, respectively, on the two datasets and outperforms the comparison methods under different imbalance ratios. Full article

(This article belongs to the Special Issue Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches)

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28 pages, 7790 KB

Open AccessEditor’s ChoiceArticle

A Hybrid Deep Learning Framework for Fault Diagnosis in Milling Machines

by Muhammad Farooq Siddique, Wasim Zaman, Muhammad Umar, Jae-Young Kim and Jong-Myon Kim

Sensors 2025, 25(18), 5866; https://doi.org/10.3390/s25185866 - 19 Sep 2025

Cited by 24 | Viewed by 1878

Abstract

This paper presents a hybrid fault-diagnosis framework for milling cutting tools designed to address three persistent challenges in industrial monitoring: noisy vibration signals, limited fault labels, and variability across operating conditions. The framework begins by removing baseline drift from raw signals to improve the signal-to-noise ratio. Logarithmic continuous wavelet scalograms are then constructed to provide precise time-frequency localization and reveal fault-related harmonics. To enhance feature clarity, a Canny edge operator is applied, suppressing minor artifacts and reducing intra-class variation so that key diagnostic structures are emphasized. Feature representation is obtained through a dual-branch encoder, where one pathway captures localized patterns while the other preserves long-range dependencies, resulting in compact and discriminative fault descriptors. These descriptors are integrated by an ensemble decision mechanism that assigns validation-guided weights to individual learners, ensuring reliable fault identification, improved robustness under noise, and stable performance across diverse operating conditions. Experimental validation on real-world cutting tool data demonstrates an accuracy of 99.78%, strong resilience to environmental noise, and consistent diagnostic performance under variable conditions. The framework remains lightweight, scalable, and readily deployable, providing a practical solution for high-precision tool fault diagnosis in data-constrained industrial environments. Full article

(This article belongs to the Special Issue Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches)

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24 pages, 4430 KB

Open AccessArticle

Early Bearing Fault Diagnosis in PMSMs Based on HO-VMD and Weighted Evidence Fusion of Current–Vibration Signals

by Xianwu He, Xuhui Liu, Cheng Lin, Minjie Fu, Jiajin Wang and Jian Zhang

Sensors 2025, 25(15), 4591; https://doi.org/10.3390/s25154591 - 24 Jul 2025

Cited by 3 | Viewed by 1294

Abstract

To address the challenges posed by weak early fault signal features, strong noise interference, low diagnostic accuracy, poor reliability when using single information sources, and the limited availability of high-quality samples in practical applications for permanent magnet synchronous motor (PMSM) bearings, this paper proposes an early bearing fault diagnosis method based on Hippopotamus Optimization Variational Mode Decomposition (HO-VMD) and weighted evidence fusion of current–vibration signals. The HO algorithm is employed to optimize the parameters of VMD for adaptive modal decomposition of current and vibration signals, resulting in the generation of intrinsic mode functions (IMFs). These IMFs are then selected and reconstructed based on their kurtosis to suppress noise and harmonic interference. Subsequently, the reconstructed signals are demodulated using the Teager–Kaiser Energy Operator (TKEO), and both time-domain and energy spectrum features are extracted. The reliability of these features is utilized to adaptively weight the basic probability assignment (BPA) functions. Finally, a weighted modified Dempster–Shafer evidence theory (WMDST) is applied to fuse multi-source feature information, enabling an accurate assessment of the PMSM bearing health status. The experimental results demonstrate that the proposed method significantly enhances the signal-to-noise ratio (SNR) and enables precise diagnosis of early bearing faults even in scenarios with limited sample sizes. Full article

(This article belongs to the Special Issue Advances in Bearing Fault Diagnosis Using Single Sensor Techniques and Sensor Fusion Approaches)

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