Crack damage monitoring for compressor blades based on acoustic emission with novel feature and hybridized feature selection. (November 2022)

• Record Type: Journal Article
• Authors: Song, Di; Xu, Feiyun; Ma, Tianchi
• Is Part Of: Structural health monitoring. Volume 21:Number 6(2022)
• Page Start: 2641
• Page End: 2656
• Publication Date: 2022-11
• Subjects: Compressor blade -- crack identification -- acoustic emission -- feature selection -- information fusion -- structural health monitoring; 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: 10.1177/14759217211068107
• Languages: English
• ISSNs: 1475-9217

Abstract

Nowadays, acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) for compressor blades. However, traditional AE features and feature selection methods are generally difficult to identify the cracked blades of compressor due to its complex structure and background noise. To solve this problem, the crack damage monitoring method based on novel feature and hybridized feature selection is proposed to identify crack of compressor blades, which is aimed at improving the crack identification accuracy with optimal features. First, the novel feature of spectral centroid with energy shift (SCES) is established. Besides, the hybridized feature selection method is proposed based on Laplacian random forest scores (LRFS), which can evaluate and select features adaptively. By fusing information of selected features from AE sensors, the long short-term memory (LSTM) network is used to classify cracked blades. The proposed method is applied experimentally to identify cracks at different speeds and locations of AE sensors, which has the average accuracy of 98.93%. The comparative results demonstrate the effectiveness and superiority of the proposed method in AE-based SHM for compressor blades under different working conditions.