Oil pump bearing fault identification method based on wavelet transform and probabilistic neural network

Shucong Liu; Hongjun Wang; Dong Zhang; Fengxia Han; Yaru Fu

doi:10.1117/12.2629093

22 April 2022 Oil pump bearing fault identification method based on wavelet transform and probabilistic neural network

Shucong Liu, Hongjun Wang, Dong Zhang, Fengxia Han, Yaru Fu

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 121740P (2022) https://doi.org/10.1117/12.2629093
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

Oil pump plays an important role in the field of oil transportation, while high-speed rotating bearings, as important parts of oil pump, often suffer various forms of damage, which poses a certain threat to the safe supply of oil resources. Based on the experimental data of bearings from Case Western Reserve University, ten types of damage data were selected for fault identification and analysis. Considering the influence of complex working conditions of the station on the collected signals, Gaussian white noise was added to the experimental data to get close to the collected signals. Based on the energy characteristics of the data obtained by wavelet transform, the probabilistic neural network is used to classify the above ten kinds of feature data. The results show that the accuracy of the classification of the proposed model is 99.76%, which is much higher than the accuracy of the current common models. The research results provide a reference method for on-site fault identification of oil pump and have a certain engineering practical significance.

Citation Download Citation

Shucong Liu, Hongjun Wang, Dong Zhang, Fengxia Han, and Yaru Fu "Oil pump bearing fault identification method based on wavelet transform and probabilistic neural network", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 121740P (22 April 2022); https://doi.org/10.1117/12.2629093

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