Fault diagnosis model based on adaptive generalised morphological filtering and LLTSA-ELM. (17th November 2022)

• Record Type: Journal Article
• Authors: Xiao, Jie; Li, Jingtao; Deng, Chao; Luo, Zhigang; Lin, Huafeng
• Is Part Of: International journal of information and communication technology. Volume 22:Number 1(2023)
• Page Start: 1
• Page End: 14
• Publication Date: 2022-11-17
• Subjects: adaptive generalised morphological filter -- linear local tangent space alignment -- LLTSA -- dimension reduction -- extreme learning machine -- ELM -- fault diagnosis; Information technology -- Periodicals; Computer science -- Periodicals; Telecommunication -- Periodicals; 004.05
• Languages: English
• ISSNs: 1466-6642

Abstract

It is difficult for a single feature to contain all the information needed to describe the running state of the equipment. Though multi-features can contain more information about running state, the redundancy between high-dimension features can easily reduce the accuracy of the classifier. Aimed at that, a fault diagnosis method for rolling bearings combining adaptive generalised morphological filter, linear local tangent space alignment and extreme learning machine (LLTSA-ELM) is proposed. Firstly, the rolling bearing vibration signals are filtered by an adaptive generalised morphological filter. Secondly, the multi-domain features are extracted from filtered signal to construct high-dimensional features set of bearing. Thirdly, the dimension of high-dimensional features is reduced by maximum likelihood estimation (MLE) and LLTSA. Finally, the bearing condition monitoring model based on ELM is constructed by the reduced dimension features, and then is used to analyse and diagnose the running state of bearing. Experiments show that this method can effectively filter noise, reduce the redundancy caused by high-dimensional features, and improve the recognition accuracy of the running state of bearings.