Acoustic emission-based condition monitoring methods: Review and application for low speed slew bearing. (May 2016)

• Record Type: Journal Article
• Authors: Caesarendra, Wahyu; Kosasih, Buyung; Tieu, Anh Kiet; Zhu, Hongtao; Moodie, Craig A.S.; Zhu, Qiang
• Is Part Of: Mechanical systems and signal processing. Volume 72/73(2016)
• Page Start: 134
• Page End: 159
• Publication Date: 2016-05
• Subjects: Acoustic emission -- Condition monitoring -- Feature extraction -- Largest Lyapunov exponent (LLE) algorithm -- Low speed slew bearing; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.10.020
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: This paper presents an acoustic emission-based method for the condition monitoring of low speed reversible slew bearings. Several acoustic emission (AE) hit parameters as the monitoring parameters for the detection of impending failure of slew bearings are reviewed first. The review focuses on: (1) the application of AE in typical rolling element bearings running at different speed classifications, i.e. high speed (>600 rpm), low speed (10–600 rpm) and very low speed (<10 rpm); (2) the commonly used AE hit parameters in rolling element bearings and (3) AE signal processing, feature extraction and pattern recognition methods. In the experiment, impending failure of the slew bearing was detected by the AE hit parameters after the new bearing had run continuously for approximately 15 months. The slew bearing was then dismantled and the evidence of the early defect was analysed. Based on the result, we propose a feature extraction method of the AE waveform signal using the largest Lyapunov exponent (LLE) algorithm and demonstrate that the LLE feature can detect the sign of failure earlier than the AE hit parameters with improved prediction of the progressive trend of the defect. Highlights: This paper presents an AE-based method for the condition monitoring of low speed slew bearing. The use of AE as the condition monitoring method of rolling element bearings is reviewed. A feature extraction method of the AE waveform signal using the largest Lyapunov exponent (LLE) algorithm is proposed.