A Neural Network Method for Bearing Fault Diagnosis

Guo, Ruifeng; Xu, Guowang; Yin, Zhenyu; Li, Jiong; Zhang, Feiqing

doi:10.1109/iccc56324.2022.10065697

Cited by 1 publication

(1 citation statement)

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“…With the rapid development of deep neural networks, various deep learning methods have been extensively applied in fault recognition and diagnosis, enabling intelligent fault diagnosis of mechanical equipment [5][6][7][8][9][10]. Deep autoencoder networks, characterized by their symmetric structure, have been widely employed in fault classification due to their unsupervised learning approach that adaptively extracts features from complex objects.…”

Section: Related Workmentioning

confidence: 99%

Novelty Detection and Fault Diagnosis Method for Bearing Faults Based on the Hybrid Deep Autoencoder Network

et al. 2023

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In the event of mechanical equipment failure, the fault may not belong to any known category, and existing deep learning methods often misclassify such faults into a known class, leading to erroneous fault diagnosis. In order to address the challenge of identifying new types of faults in mechanical equipment fault diagnosis, this paper proposes a novelty detection and fault diagnosis method for bearing faults based on a hybrid deep autoencoder network. Firstly, a hybrid deep autoencoder network with one input and two outputs was constructed. The original data were then fed into the network to obtain its low-dimensional representation and reconstructed data. By setting a threshold based on the reconstruction error, novel class faults can be detected, while known faults can be classified based on low-dimensional features. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 98.59% (100%) for novel class identification (known fault classification) on the CWRU bearing dataset, 96.79% (98.53%) on the Paderborn dataset, and 84.34% (97.03%) on the MFPT dataset. Therefore, the hybrid deep autoencoder network not only accurately detects unknown types of faults but also effectively classifies known fault types, demonstrating excellent fault identification and classification capabilities.

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