Time-frequency Signal Analysis in Machinery Fault Diagnosis: Review K.H. Hui 1,a* , Lim Meng Hee 2,b , M. Salman Leong 3,c and Ahmed M. Abdelrhman 4,d 1,2 RAZAK School of Engineering & Advanced Technology, Universiti Teknologi Malaysia, Malaysia 3,4 Faculty of Mechanical Engineering, Universiti Teknologi Malaysia, Malaysia a karhoou@yahoo.com, b mhlim@ic.utm.my, c salman@ic.utm.my, d ahmedrabak@yahoo.com Keywords: Decomposition; Time-frequency Analysis; Wavelet; Machinery; Fault Diagnosis. Abstract. Growing demand of machines such as gas turbine, pump, and compressor in power generation, aircraft, and other fields have yielded the transformation of machine maintenance strategy from corrective and preventive to condition-based maintenance. Real-time fault diagnosis has grabbed attention of researchers in looking for a better approach to overcome current limitation. The parameters of health condition in machinery could be monitored thus faults could be detected and diagnosed by using signal analysis approach. Since some fault signals are non-stationary or time dependent in nature, therefore time-frequency signal analysis is crucial for machinery fault diagnosis. Common time-frequency signal analysis methods are such as short time Fourier transform (STFT), wavelets analysis, empirical mode decomposition (EMD), Hilbert-Huang transform (HHT), etc. This review provides a summary of the basic principle of signal analysis, the most recent researches, and some advantages and limitations associated to each types of time- frequency signal analysis method. Introduction Machinery condition monitoring and fault diagnosis have becoming more important with the ever increasing needs of critical and advanced machines in power generation, aviation, chemical industry, manufacturing industry, etc. Analysis of vibration signals in condition monitoring and fault diagnosis has been practiced for decades. Vibration signal is typically analysed using Fast Fourier Transform (FFT). However its capability to analyse non-stationary signals is very limited. Therefore, time-frequency signal analysis has been developed to overcome the shortcoming of FFT- based methods [1]. Short Time Fourier Transform (STFT) represents the earliest stage of time- frequency signal analysis technique. It is an important breakthrough in time-frequency signal analysis. STFT is localized in time and frequency simultaneously and therefore overcomes the drawbacks of Fourier Transform based methods in processing non-stationary signals. Wavelet transform (WT) was subsequently introduced to improve the signal analysis capability of STFT. WT has gained much attention from researchers around the world since last decade. A significant number of publications were published. Wavelet analysis was used to analyse gearbox fault [2], ultrasonic measurement of liquid-layer thickness [3], beam damage diagnosis [4], and etc. Recently, a relatively new self-adaptive signal analysis method namely the Empirical Mode Decomposition (EMD) is being developed to analyze non-stationary signal. It provides an alternative for researchers in non-stationary signal processing. This review aims to provide a brief review on the subject of time-frequency signal analysis for machinery fault diagnosis application. Short Time Fourier Transform (STFT) The disadvantages of Fast Fourier Transform in processing non-stationary signals is overcome by the introduction of Short Time Fourier Transform (STFT) which is localized in both time and frequency concurrently [1]. The mathematical model of STFT is given in Eq. 1; [5]. = − e d (1) Advanced Materials Research Vol. 845 (2014) pp 41-45 © (2014) Trans Tech Publications, Switzerland doi:10.4028/www.scientific.net/AMR.845.41 All rights reserved. No part of contents of this paper may be reproduced or transmitted in any form or by any means without the written permission of TTP, www.ttp.net. (ID: 161.139.152.245-22/11/13,10:09:52)