MSc Electrical & Computer Engineering Defense Notice

Event Date: 
Friday, December 13, 2019 - 9:00am to 11:30am
Event Location: 
ATAC 1003
Event Contact Name: 
Femi Mirshekari
Event Contact E-mail: 

Presented by: Aman Shukla
Supervisor: Dr. Wilson Wang

A Selective Teager Huang Transform Technique for Bearing Fault Detection

ABSTRACT

Rolling element bearings are commonly used in rotary mechanical and electrical equipment. Online health condition monitoring plays a vital role in recognizing the bearing defect at its early stage so as to prevent machinery performance degradation and malfunction. According to investigation, more than half of the rotary machine faults are related to bearing defects. Although many signal processing techniques have been suggested in literature for bearing fault detection, reliable bearing fault diagnosis still remains a challenging task. One of the reasons is that bearing is not a simple component like a gear or a shaft, but a complex system; vibration signals generated from a faulty bearing are usually nonstationary, especially when there is a slippage. However, nonstationary signals are much difficult to analyze using classical signal processing techniques. The objective of this study is to develop an online condition monitoring system for bearing fault detection. In this work, a smart sensor-based data acquisition (DAQ) system is developed for vibration data collection, which consists of a sensing unit, a signal conditioning circuit and a wireless communication module. A selective Teager-Huang transform technique is proposed for bearing fault detection. The processing is undertaken in three steps: Firstly, a denoising filter is used to improve the signal to noise ratio; secondly, a correlation function method is suggested to choose intrinsic mode functions with most representative features related to the fault; and thirdly, a generalized Teager-Kaiser spectrum method is proposed to synthesize the extracted intrinsic mode functions for bearing fault detection. The bearing fault characteristic signatures can be identified from the energy spectrum. The accuracy of the developed smart sensor-based DAQ prototype is examined by comparison tests. The effectiveness of the proposed selective Teager-Huang transform technique is verified by experimental tests corresponding to different bearing conditions. Its robustness in bearing fault detection is examined by the use of the data sets from a different experimental setup.

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