Identification of tooth fault in a gearbox based on cyclostationarity and empirical mode decomposition. (May 2018)

• Record Type: Journal Article
• Authors: Kim, Jong-Sik; Lee, Sang-Kwon
• Is Part Of: Structural health monitoring. Volume 17:Number 3(2018)
• Page Start: 494
• Page End: 513
• Publication Date: 2018-05
• Subjects: Fault diagnosis -- gearbox -- cyclostationary -- intrinsic mode function -- empirical mode decomposition; 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: 10.1177/1475921717704384
• Languages: English
• ISSNs: 1475-9217

Abstract

In the previous work, the cyclostationarity process, which is one of signal processing methods, has been used in health monitoring of the rotating machinery because of the superior detecting property of hidden periodicity. However, it is often difficult to acquire the information about the hidden periodicity due to the fault of the rotating machinery when the impact signal is low. Therefore, a certain preprocessing tool to extract the information about the impact signal due to the fault is required. This article presents the new detection process of tooth faults in a gearbox system based on the empirical mode decomposition algorithm which adaptively decomposes the signal into a set of intrinsic mode functions and the cyclostationarity process which identifies the hidden periodicity clearly in bi-frequency domain. The proposed method was demonstrated with a simulated signal and was applied to the detection of four types of conditions of tooth fault successfully.