Time-frequency space vector modulus analysis of motor current for planetary gearbox fault diagnosis under variable speed conditions. (15th April 2019)

• Record Type: Journal Article
• Authors: Chen, Xiaowang; Feng, Zhipeng
• Is Part Of: Mechanical systems and signal processing. Volume 121(2019)
• Page Start: 636
• Page End: 654
• Publication Date: 2019-04-15
• Subjects: Fault diagnosis -- Induction motors -- Variable speed -- Planetary gearbox -- Space vector analysis; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2018.11.049
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: Both amplitude and phase modulations are considered in current signal model. Time-varying spectrum of squared space vector modulus is derived. It overcomes difficulty due to dominant power supply frequency and intricate sidebands. It is effective in extracting the time-varying fault characteristic frequencies. Abstract: Motor current signature analysis is a promising technique for electromechanical system fault detection, and has been studied under steady states. In this paper, a planetary gearbox fault diagnosis method under variable speed conditions using stator current signal is proposed. In order to thoroughly understand the frequency characteristics of current signals, an analytical amplitude modulation and phase modulation (AM-PM) current model considering gear fault modulation effects is presented. Two more aspects of endeavor are made to highlight gear fault signatures in stator current signals in context of inconspicuousness and time variability. Firstly, to address the sideband complexity and power supply dominance issues inherent with stator current signals, squared space vector modulus (SVM) together with its time-varying spectral characteristics in planetary gearbox fault cases under variable speed conditions are mathematically derived. Secondly, to reveal time-varying fault features in details, polynomial chirplet transform (PCT) is improved by iterative algorithm, and merits of fine time-frequency resolution and cross term free nature are achieved. The effectiveness of the proposed method is illustrated by numerical simulation, and is further validated by lab experiments on a real world 4 kW induction motor driven planetary gearbox test rig.