An audio-based intelligent fault diagnosis method for belt conveyor rollers in sand carrier. (December 2020)

• Record Type: Journal Article
• Authors: Peng, Chen; Li, ZhiPeng; Yang, Minjing; Fei, Minrui; Wang, Yulong
• Is Part Of: Control engineering practice. Volume 105(2020)
• Publication Date: 2020-12
• Subjects: Wavelet packet decomposition -- Convolutional neural network -- Roller faults -- Energy spectrum -- Sand carrier; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2020.104650
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: The roller is an important part of the belt conveyor in sand carrier at sea. A good fault diagnosis method of the rollers provides an effective guarantee for the system's optimal operation. In this paper, a novel intelligent fault diagnosis method for rollers is proposed by using audio wavelet packet decomposition and Convolutional Neural Networks (CNN). Firstly, the wavelet packet decomposition algorithm is used to decompose the audio data of the rollers into several frequency bands. Secondly, the lowest frequency data are adjusted under consideration of the excessive energy proportion of the low frequency data. Then, CNN is used to classify the features of each frequency band to diagnose rollers' faults. The experiment shows that the diagnosis method has high accuracy, high speed and strong robustness, which greatly improves the efficiency of fault diagnosis of rollers of sand carrier. Graphical abstract: Highlights: The wavelet packet and CNN were unified used to diagnose roller faults. A new eigenvalue extraction method is proposed to improve the classification effect. The method proposed in this paper can greatly reduce the number of personnel required.