Multiple hierarchical compression for deep neural network toward intelligent bearing fault diagnosis

Sun, Junhua; Liu, Zhao; Wen, Jia; Fu, Rongrong

doi:10.1016/j.engappai.2022.105498

Cited by 23 publications

(5 citation statements)

References 36 publications

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“…In addition to the benchmark datasets, we also collected both vibration and acoustic signals from our own rolling element-bearing experimental platform, presented in Figure 6. In this bearing experimental platform, both the vibration and acoustic signals were simultaneously collected from the accelerometer sensor and preamplifier sensor, Similar to the CWRU dataset, the signals are collected from normal, inner race fault, outer race fault, and ball element fault conditions because these three faults are the most representative fault types [54,55]. These three faults in the bearing were generated by an electrical discharge machine.…”

Section: Target Signals (Normal Signals)mentioning

confidence: 99%

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Machinery Fault Signal Detection with Deep One-Class Classification

Yoon,

2023

Applied Sciences

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Fault detection of machinery systems is a fundamental prerequisite to implementing condition-based maintenance, which is the most eminent manufacturing equipment system management strategy. To build the fault detection model, one-class classification algorithms have been used, which construct the decision boundary only using normal class. For more accurate one-class classification, signal data have been used recently because the signal data directly reflect the condition of the machinery system. To analyze the machinery condition effectively with the signal data, features of signals should be extracted, and then, the one-class classifier is constructed with the features. However, features separately extracted from one-class classification might not be optimized for the fault detection tasks, and thus, it leads to unsatisfactory performance. To address this problem, deep one-class classification methods can be used because the neural network structures can generate the features specialized to fault detection tasks through the end-to-end learning manner. In this study, we conducted a comprehensive experimental study with various fault signal datasets. The experimental results demonstrated that the deep support vector data description model, which is one of the most prominent deep one-class classification methods, outperforms its competitors and traditional methods.

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Section: Target Signals (Normal Signals)mentioning

confidence: 99%

“…Similar to the CWRU dataset, the signals are collected from normal, inner race fault, outer race fault, and ball element fault conditions because these three faults are the most representative fault types [54,55]. These three faults in the bearing were generated by an electrical discharge machine.…”

Section: Target Signals (Normal Signals)mentioning

confidence: 99%

Machinery Fault Signal Detection with Deep One-Class Classification

Yoon,

2023

Applied Sciences

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“…In addition, one of the new trends in bearing fault detection and diagnosis is optimizing the machine learning model (model capacity, computational resources, inference latency) so that it can be deployed on constrained hardware [17]. Regarding this trend, there are several popular approaches such as neural network architecture search [18][19][20][21] or model compression using pruning [17,[21][22][23], quantization [23][24][25][26], and knowledge distillation [24,[27][28][29]. Each approach will have different advantages and disadvantages, so how to use them should be carefully considered.…”

Section: Introductionmentioning

confidence: 99%

Efficient bearing fault diagnosis with neural network search and parameter quantization based on vibration and temperature

et al. 2023

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In this work, we propose a deep-learning method to diagnose bearing faults of electric motors based on vibration and bearing housing temperature. Our methods can accurately diagnose faults related to bearing cracking and lubricant shortages. The proposed method is effective in terms of computational complexity and model capacity thanks to the advantages of neural architecture search (NAS) and parameter quantization in the model establishment. The experimental results found that the information on bearing temperature improved the diagnostic accuracy for the bearing fault diagnosis task. The proposed method has explored the most optimal model in terms of computational resources and model capacity with a pre-defined accuracy target. The searched model has a relatively high diagnostic accuracy of 98.7% and a size of about 27.3 kB. After quantization, the obtained model maintained 96.9% accuracy and reduced 4 times in size. All experiments are executed elaborately on our custom bearing fault dataset.

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“…4 Hence, it is of great scientific and realistic engineering importance to ascertain bearing faults promptly and to avoid continuous deterioration of bearing faults. [5][6][7] In the early days of the popularity of data-driven rolling bearing fault diagnosis methods, scholars, and experts adopted some machine learning algorithms to recognize faults. 8,9 Common diagnostic methods of machine learning include random forest (RF), 10 support vector machine (SVM) 11 , and K-nearest neighbor algorithm (KNN).…”

Section: Introductionmentioning

confidence: 99%

“…4 Hence, it is of great scientific and realistic engineering importance to ascertain bearing faults promptly and to avoid continuous deterioration of bearing faults. 5–7…”

Section: Introductionmentioning

confidence: 99%

A robust multi-scale learning network with quasi-hyperbolic momentum-based Adam optimizer for bearing intelligent fault diagnosis under sample imbalance scenarios and strong noise environment

Ye,

Yan,

Chen

et al. 2023

Structural Health Monitoring

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Due to adverse working conditions of rotating machinery in actual engineering, bearing fault data are more difficult to acquire compared to normal data. That said, the real collected bearing vibration data are usually characterized by imbalance. Meanwhile, fault information of the raw collected bearing vibration data is effortlessly drowned out by strong noises, which indicates that it is awkward to efficiently recognize bearing fault states via using traditional fault diagnosis methods under this background. To overcome these problems, this research proposes an individual approach formally intituled as robust multi-scale learning network (RMSLN) with quasi-hyperbolic momentum-based Adam (QHAdam) optimizer for bearing fault diagnosis, which mainly includes convolution-pooling operation, multi-scale branch, and classification layer. Within the proposed method, the channel attention mechanism based on squeezed excitation network is embedded into the multi-scale branch in the form of residual connections, which not only reinforce important information and weaken noise interference, but also capture fault features more comprehensively and enhance the discrimination of fault states with fewer samples. Additionally, in the training process, QHAdam optimizer is introduced to tightly control the loss of RMSLN to enable a faster and smoother convergence. Two groups of experimental bearing data are studied to support the availability of presented approach, and several traditional fault diagnosis methods and representative imbalance fault diagnosis approaches are compared in four evaluation metrics (accuracy, macro-precision, macro-recall, and macro-F1 score) to highlight the advantages of the presented method.

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Multiple hierarchical compression for deep neural network toward intelligent bearing fault diagnosis

Cited by 23 publications

References 36 publications

Machinery Fault Signal Detection with Deep One-Class Classification

Machinery Fault Signal Detection with Deep One-Class Classification

Efficient bearing fault diagnosis with neural network search and parameter quantization based on vibration and temperature

A robust multi-scale learning network with quasi-hyperbolic momentum-based Adam optimizer for bearing intelligent fault diagnosis under sample imbalance scenarios and strong noise environment

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