Rotating machinery fault identification method based on the cloud model confronting unbalanced data

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Journal of Vibration and Shock ›› 2022, Vol. 41 ›› Issue (22) : 70-78.

ZHAO Nan，ZHAO Rongzhen

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Abstract

The data imbalance caused by the abundant normal data and the lack of fault data has become one of the key problems faced by the industrial big data intelligent decision-making technology. Therefore, in order to solve the problem that the recognition accuracy of a small number of sample categories in imbalanced data sets is often encountered in the research of mechanical equipment fault diagnosis, an ensemble learning method based on cloud model is proposed and used for pattern recognition of unbalanced data of rotating machinery. First, The method uses the ReliefF algorithm to calculate the weight of each feature from the extracted bearing fault feature data set. According to the result of the feature weight value in descending order, features with higher weights are extracted to form a low-dimensional feature set, and the low-dimensional feature set is divided into unbalanced training set and test set. Secondly, through the forward cloud generator and reverse cloud generator in the cloud model theory, each feature in the low-dimensional feature set is drawn separately to obtain the training data and test data cloud diagrams of each state under a single feature. Then, the distance formula is used to discriminate the training data cloud image with the closest distance to the sample to be tested, and the category of the sample to be tested under a single feature is judged. Finally, the recognition results under each feature are integrated through the ensemble learning method, and the relative majority voting method is used to identify the sample to be tested. Method of this article compared with the traditional BP neural network and support vector machine, the experiment shows that this method not only has high recognition accuracy for the unbalanced data to be tested, but also has a certain generalization performance.
Key words: Fault identification; Unbalanced data; Cloud model; ReliefF algorithm; Ensemble learning

Key words

Fault identification / Unbalanced data / Cloud model / ReliefF algorithm / Ensemble learning

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ZHAO Nan，ZHAO Rongzhen. Rotating machinery fault identification method based on the cloud model confronting unbalanced data[J]. Journal of Vibration and Shock, 2022, 41(22): 70-78

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