A New Generative Neural Network for Bearing Fault Diagnosis with Imbalanced Data

You, Wei; Shen, Changqing; Chen, Liang; Que, Hongbo; Huang, Weiguo; Zhu, Zhongkui

doi:10.1155/2020/8869648

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…The experiment of model training only performs the LSTM neural network with the dataset we created. To prove the model is appropriate for the applications, it might require conducting different neural networks to verify the performance, such as in the work conducted by Wei You et al, 2020 [32], or checking the loss function and accuracy. Here we adopt the cross-entropy loss function for validation.…”

Section: Loss and Accuracymentioning

confidence: 99%

Failure Mode Detection and Validation of a Shaft-Bearing System with Common Sensors

Kuo

Chuang

Liang

2022

Sensors

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Failure mode detection is essential for bearing life prediction to protect the shafts on the machinery. This work demonstrates the rolling bearing vibration measurement, signals converting and analysis, feature extraction, and machine learning with neural networks to achieve failure mode detection for a shaft bearing. Two self-designed bearing test platforms with two types of sensors conduct the bearing vibration collection in normal and abnormal states. The time-domain signals convert to the frequency domain for analysis to observe the dominant frequency between these two types of sensors. In feature extraction, principal components analysis (PCA) combines with wavelet packet decomposition (WPD) to form the two feature extraction methods: PCA-WPD and WPD-PCA for optimization. The features extracted by these two methods serve as input to the long short-term memory (LSTM) networks for classification and training to distinguish bearing states in normal, misaligned, unbalanced, and impact loads. The evaluation arguments include sensor types, vibration directions, failure modes, feature extraction methods, and neural networks. In conclusion, the developed methods with the typical lower-cost sensor can achieve 97% accuracy in bearing failure mode detection.

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Section: Loss and Accuracymentioning

confidence: 99%

Failure Mode Detection and Validation of a Shaft-Bearing System with Common Sensors

Kuo

Chuang

Liang

2022

Sensors

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“…In the context of big data, fault diagnosis methods based on data-driven are undoubtedly the most popular research hotspot. 2 In the early stage, many fault diagnosis methods based on machine learning (ML) are explored. Hajnayeb et al 3 used artificial neural networks (ANN) for gearbox fault diagnosis and optimized the whole system with feature selection methods, achieving good results.…”

Section: Introductionmentioning

confidence: 99%

Intelligent machine fault diagnosis based on deep transfer convolutional neural network and extreme learning machine

Cen

Chen

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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With the in-depth study of researchers in the field of fault diagnosis, many machine fault diagnosis methods based on deep learning have been proposed. These methods have achieved remarkable results, but some practical issues still should be solved, such as lack of sufficient training data with labels and long training time. In this research, a machine fault diagnosis method using deep transfer convolutional neural network (DTCNN) and extreme learning machine (ELM) is proposed. Firstly, continuous wavelet transform (CWT) is adopted to transform vibration signals into 2D time-frequency images. Then, the optimal DTCNN pre-trained by ImageNet dataset is selected to extract high-level features of time-frequency images. The extracted high-level features further are input to the ELM classifier for fault classification. Finally, the extracted high-level features further are input to the ELM classifier for fault classification. The effectiveness and efficiency of the proposed method are verified on two well-known datasets, including the Case Western Reserve University (CWRU) motor bearing dataset and the KAt bearing dataset of Paderborn University. The experimental results show that the proposed method can greatly reduce the computational time of the model while ensuring high accuracy of diagnosis, and DTCNN-ELM outperforms other state-of-the-art methods.

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A New Generative Neural Network for Bearing Fault Diagnosis with Imbalanced Data

Cited by 2 publications

References 31 publications

Failure Mode Detection and Validation of a Shaft-Bearing System with Common Sensors

Failure Mode Detection and Validation of a Shaft-Bearing System with Common Sensors

Intelligent machine fault diagnosis based on deep transfer convolutional neural network and extreme learning machine

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