Railway bearing and cardan shaft fault diagnosis via an improved morphological filter. (September 2020)

• Record Type: Journal Article
• Authors: Li, Yifan; Zuo, Ming J; Chen, Zaigang; Lin, Jianhui
• Is Part Of: Structural health monitoring. Volume 19:Number 5(2020)
• Page Start: 1471
• Page End: 1486
• Publication Date: 2020-09
• Subjects: Morphological filter -- morphological operator -- filtering scale -- train fault; 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: 10.1177/1475921719886067
• Languages: English
• ISSNs: 1475-9217

Abstract

Railway faults are usually observed as impulses in the vibration signal, but they are mostly immersed in noise. To effectively remove noise and identify the impulses, an improved morphological filter is proposed in this article. The proposal focuses on two aspects: a novel gradient convolution operator is proposed for feature extraction, and a new fault sensitivity measurement algorithm is proposed for scale selection because a morphological filter's effectiveness is mainly determined by these two elements. The performance of the improved morphological filter is evaluated with real vibration signals measured from train's axle bearings and cardan shafts. From the analysis of three sets of railway faults, the results indicate that the proposed morphological filter effectively detects the faults. Compared with three reported morphological filters, the proposed method has better diagnosis effectiveness.