Research on diagnosis algorithm of mechanical equipment brake friction fault based on MCNN-SVM. (December 2021)

• Record Type: Journal Article
• Authors: Zhang, Xunjie; Zhang, Min; Xiang, Zaiyu; Mo, Jiliang
• Is Part Of: Measurement. Volume 186(2021)
• Publication Date: 2021-12
• Subjects: Mechanical equipment -- Brake friction -- Modified convolutional neural network -- Support vector machine -- Fault diagnosis; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.110065
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: The issues of brake friction showing multi-source and small samples are solved. An intelligent diagnosis algorithm based on MCNN-SVM is proposed. The performance of this method is validated in the data of brake friction. The superiority of the proposed method is verified. Abstract: Brakes in mechanical equipment are crucial for operational safety, and their effects are directly affected by friction performance. The fault signal induced by friction interface presents the phenomenon of multi-source, and the fault samples are difficult to obtain in practical engineering. Both aspects yield unsatisfactory recognition performance of diagnosis models. To address the issues, in this article, we proposed an algorithm based on a modified convolutional neural network (CNN) and support vector machine (SVM). First, dynamic features were extracted from the friction factor and friction surface temperature as samples, which could effectively present the state of brake friction. Next, CNN was used to learn feature knowledge from dynamic feature set, the Mish activation function, batch normalisation and dropout were employed to complete the training of modified CNN (MCNN). Then, the dynamic feature set was input into the trained MCNN again to learn the feature representations of friction state. Finally, the feature representations were migrated to SVM to establish the mapping between feature space and label space, and the final fault recognition was completed. The proposed algorithm fully combined the powerful feature learning ability of MCNN and the excellent classification performance of SVM on small samples. Experiment results showed that MCNN-SVM had faster convergence speed, and the accuracy of the proposed algorithm reached 100%. Its diagnosis effect was better than counterpart algorithms.