Fault feature recognition of centrifugal compressor with cracked blade based on SNR estimation and adaptive stochastic resonance. (January 2023)

• Record Type: Journal Article
• Authors: Song, Di; Xu, Feiyun; Hu, Jianzhong; Ma, Tianchi
• Is Part Of: Structural health monitoring. Volume 22:Number 1(2023)
• Page Start: 131
• Page End: 150
• Publication Date: 2023-01
• Subjects: Centrifugal compressor -- SNR estimation -- feature recognition -- stochastic resonance -- fault diagnosis; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/14759217221084880
• Languages: English
• ISSNs: 1475-9217

Abstract

The centrifugal compressor is widely used in modern industry, and the blades are prone to crack due to complex loads and working conditions. Owing to the fault features caused by initial blade cracks are week and easily interfered by strong noise, it is difficult to recognize accurately by traditional methods. In this study, the SNR estimation and adaptive stochastic resonance (SEASR) method is proposed for fault feature recognition of centrifugal compressor with cracked blade. Based on the relationship between SNR and stochastic resonance (SR) parameters, the subtract noise by empirical mode decomposition (SNEMD) method is established with second-order and fourth-order moment (M2M4), hence solving the problem of adaptive SR. The effectiveness of SEASR is tested and analyzed by simulation signals and experimental data. The results demonstrate that the proposed method can accurately recognize fault features of cracked blades and compound faults, where the output SNR is superior to that of the previous methods. It is a new method to realize fault diagnosis for centrifugal compressor with cracked blade and other rotating machinery.