Title: Optimal wavelet analysis and enhanced independent component analysis for isolated and combined mechanical faults diagnosis
Authors: Tawfik Thelaidjia; Salah Chenikher; Abdelkrim Moussaoui
Addresses: LABGET Laboratory, Department of Electrical Engineering, Faculty of science and Technology, Larbi Tebessi University, Tebessa, Algeria ' LABGET Laboratory, Department of Electrical Engineering, Faculty of science and Technology, Larbi Tebessi University, Tebessa, Algeria ' Laboratory of Electrical Engineering of Guelma (LGEG), University 8 May 1945 Guelma, Algeria
Abstract: In this paper, a new approach is suggested for isolated and combined mechanical faults diagnosis. The suggested approach consists of two main steps: vibration signal denoising and characteristic frequency extracting. Firstly, an optimal wavelet multi-resolution analysis is employed for reducing noise from vibration signals. Secondly, the enhanced independent component analysis (EICA) algorithm which overcomes the shortcoming of the ICA algorithm and allows selecting the reliable independent components is adopted for source separation. Therefore, simple and comprehensible spectra will be obtained. Finally, the suggested method is tested using real vibration signals. Compared with other approaches, it has been revealed that the suggested method can efficiently be employed to diagnose both isolated and combined mechanical faults.
Keywords: Hilbert transform; fault diagnosis; enhanced independent component analysis; EICA; mechanical faults; tri-axial accelerometer; optimal wavelet multi-resolution analysis.
International Journal of Advanced Mechatronic Systems, 2020 Vol.8 No.2/3, pp.116 - 126
Received: 28 Feb 2020
Accepted: 01 Jun 2020
Published online: 19 Nov 2020 *