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The multilabel fault diagnosis model of bearing based on integrated convolutional neural network and gated recurrent unit

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International Journal of Intelligent Computing and Cybernetics 2022年第3期15卷 401-413页

作者： Shanling Han Shoudong Zhang Yong Li Long Chen College of Mechanical and Electronic Engineering Shandong University of Science and TechnologyQingdaoChina College of Materials Science and Engineering Shandong University of Science and TechnologyQingdaoChina

Purpose-Intelligent diagnosis of equipment faults can effectively avoid the shutdown caused by equipment faults and improve the safety of the *** present,the diagnosis of various kinds of bearing fault information,such as the occurrence,location and degree of fault,can be carried out by machine learning and deep learning and realized through the multiclassification ***,the multiclassification method is not perfect in distinguishing similar fault categories and visual representation of fault *** improve the above shortcomings,an end-to-end fault multilabel classification model is proposed for bearing fault ***/methodology/approach-In this model,the labels of each bearing are binarized by using the binary relevance ***,the integrated convolutional neural network and gated recurrent unit(CNN-GRU)is employed to classify *** from the general CNN networks,the CNN-GRU network adds multiple GRU layers after the convolutional layers and the pool ***-The Paderborn University bearing dataset is utilized to demonstrate the practicability of the *** experimental results show that the average accuracy in test set is 99.7%,and the proposed network is better than multilayer perceptron and CNN in fault diagnosis of bearing,and the multilabel classification method is superior to the multiclassification ***,the model can intuitively classify faults with higher ***/value-The fault labels of each bearing are labeled according to the failure or not,the fault location,the damage mode and the damage degree,and then the binary value is *** multilabel problem is transformed into a binary classification problem of each fault label by the binary relevance method,and the predicted probability value of each fault label is directly output in the output layer,which visually distinguishes different fault conditions.

关键词： Intelligent fault diagnosis Bearing fault Multilabel classification CNN-GRU binary relevance method

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