Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6955342 | Mechanical Systems and Signal Processing | 2016 | 26 Pages |
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 (>600Â rpm), low speed (10-600Â rpm) and very low speed (<10Â 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.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Wahyu Caesarendra, Buyung Kosasih, Anh Kiet Tieu, Hongtao Zhu, Craig A.S. Moodie, Qiang Zhu,