Bearing fault diagnosis using transfer learning and optimized deep belief network

Zhao, Huimin; Yang, Xiaoxu; Chan, Chi Hou; Chen, Huayue; Deng, Wu

doi:10.1088/1361-6501/ac543a

Cited by 97 publications

(51 citation statements)

References 56 publications

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“…To verify the effectiveness of the method in this paper, two classic methods (SVM [ 41 ] and DBN [ 42 ]) and three latest methods (SPBO-SDAE [ 11 ], PSO-DNN [ 12 ] and CS-IMSNs [ 13 ]) were selected for comparison. The brief settings of these fault diagnosis methods are listed in Table 5 .…”

Section: Experimental Results and Analysismentioning

confidence: 99%

Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines

Shan

Bao

et al. 2022

Sensors

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With the complexity and refinement of industrial systems, fast fault diagnosis is crucial to ensuring the stable operation of industrial equipment. The main limitation of the current fault diagnosis methods is the lack of real-time performance in resource-constrained industrial embedded systems. Rapid online detection can help deal with equipment failures in time to prevent equipment damage. Inspired by the ideas of compressed sensing (CS) and deep extreme learning machines (DELM), a data-driven general method is proposed for fast fault diagnosis. The method contains two modules: data sampling and fast fault diagnosis. The data sampling module non-linearly projects the intensive raw monitoring data into low-dimensional sampling space, which effectively reduces the pressure of transmission, storage and calculation. The fast fault diagnosis module introduces the kernel function into DELM to accommodate sparse signals and then digs into the inner connection between the compressed sampled signal and the fault types to achieve fast fault diagnosis. This work takes full advantage of the sparsity of the signal to enable fast fault diagnosis online. It is a general method in industrial embedded systems under data-driven conditions. The results on the CWRU dataset and real platforms show that our method not only has a significant speed advantage but also maintains a high accuracy, which verifies the practical application value in industrial embedded systems.

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Section: Experimental Results and Analysismentioning

confidence: 99%

Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines

Shan

Bao

et al. 2022

Sensors

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“…Ensuring their flawless operation demands the swift diagnosis of faults and accurate prediction of system states, particularly in the context of complex machinery such as rotating industrial equipment and cement structures (Zhao et al, 2022a). Traditional approaches to fault detection and diagnosis, which involve professional maintenance personnel and expensive training, underscore the substantial economic and logistical challenges associated with maintaining such industrial systems (Zhao et al, 2022b). In this light, the need for innovative, costeffective and efficient solutions is more critical than ever (Jiao, 2021).…”

Section: Introductionmentioning

confidence: 99%

Interactive spatiotemporal LSTM approach for enhanced industrial fault diagnosis

Zhang,

Huang,

et al. 2024

ILT

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Purpose Rotating machinery is a crucial component of large equipment, and detecting faults in it accurately is critical for reliable operation. Although fault diagnosis methods based on deep learning have been significantly developed, the existing methods model spatial and temporal features separately and then weigh them, resulting in the decoupling of spatiotemporal features. Design/methodology/approach The authors propose a spatiotemporal long short-term memory (ST-LSTM) method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification. Findings Through these two experiments, the authors demonstrate that machine learning methods still have advantages on small-scale data sets, but our proposed method exhibits a significant advantage due to the simultaneous modeling of the time domain and space domain. These results indicate the potential of the interactive spatiotemporal modeling method for fault diagnosis of rotating machinery. Originality/value The authors propose a ST-LSTM method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

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“…Given their prolonged exposure to harsh environments characterized by alternating working conditions, high speeds, heavy loads, and intense vibrations, rolling bearings may experience wear, cracks, breakages, and other failures that can reduce production efficiency or pose risks to personnel safety [2]. Therefore, intelligent fault diagnosis technology for rolling bearings plays a crucial role in the timely predicting and localization of faults during their early stages, which is crucial for enhancing the safety of rotating machinery systems [3].…”

Section: Introductionmentioning

confidence: 99%

A clustering multi-stage training transfer learning framework for cross simulation domain and experimental domain fault diagnosis

Yu,

Liu,

Zhao

et al. 2024

Meas. Sci. Technol.

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Deep learning methods have demonstrated remarkable achievements in the field of fault diagnosis for rotating machinery. However, their effectiveness heavily relies on high-quality labeled samples, which presents a significant challenge owing to the limited availability of such data in engineering applications. To address this realistic issue, we propose a novel simulation-driven transfer learning model called the Clustering Multi-Stage Training Transfer Learning Framework(CMSTL) for fault diagnosis of rolling bearings. The fundamental concept of the proposed method is to utilize simulation data as a substitute for labeled actual device data and integrate the suggested clustering learning and multistage training strategies to extract domain-independent and fault-discriminative features from simulation and experimental domains. Specifically, the clustering learning strategy is embedded into the CMSTL model to encourage the feature extractor to acquire distinguishable features associated with different categories while eliminating domain-specific knowledge, which enables samples near the classification decision boundary to cluster towards their respective clustering centers. Additionally, the proposed multistage learning strategy leverages the model trained with a certain level of accuracy in the first stage to annotate actual device samples, thereby enhancing both the precision of the pseudo-label for real data and the overall training stability of the model. The effectiveness and superiority of the proposed method were validated using both artificially damaged and run-to-failure datasets. The comparative analysis results demonstrate that the CMSTL method exhibits a minimum 2.36% improvement in fault diagnosis accuracy and enhances the clustering capability compared to other advanced transfer learning fault diagnosis methods.

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Bearing fault diagnosis using transfer learning and optimized deep belief network

Cited by 97 publications

References 56 publications

Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines

Fast Fault Diagnosis in Industrial Embedded Systems Based on Compressed Sensing and Deep Kernel Extreme Learning Machines

Interactive spatiotemporal LSTM approach for enhanced industrial fault diagnosis

A clustering multi-stage training transfer learning framework for cross simulation domain and experimental domain fault diagnosis

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