Impulse detection using a shift-invariant dictionary and multiple compressions. (9th June 2019)

• Record Type: Journal Article
• Authors: Lin, Huibin; Tang, Jianmeng; Mechefske, Chris
• Is Part Of: Journal of sound and vibration. Volume 449(2019)
• Page Start: 1
• Page End: 17
• Publication Date: 2019-06-09
• Subjects: Compressive sensing -- Shift-invariant dictionary -- Impulse detection -- Localized faults; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2019.02.027
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: Impulse components in vibration signals are regarded as a symptom of localized damage in rotary machines. When influenced by excessive noise, it is difficult to detect these impulses, especially from under-sampled measurements. In this paper, a method called Multi-Compressive Matching Pursuit (MCMP) is developed for impulse component detection. A shift-invariant dictionary is constructed to sparsely represent the impulse components caused by localized faults. Impulse features are enhanced by integrating multi-channel compressive samples using the sum-proxy strategy. The impulse components are then sparsely reconstructed from noisy compressive samples. The performance of the proposed method is compared with the Compressive Sampling Matching Pursuit (CoSaMP) method using numerical simulations. The advantage of the proposed method is further validated through detecting localized faults on gears and roller bearings. The simulation and experimental results also prove that the proposed method can effectively reconstruct the impulse components under slowly time-varying conditions. Highlights: A Multi-Compressive Matching Pursuit method for impulse component detection. Impulse features are enhanced by the sum-proxy strategy. It can be used in variable speed conditions. It can be used for detecting localized faults on gear and bearing.