Time-Frequency Signal Analysis in Machinery Fault Diagnosis: Review

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.

• Periodical: Advanced Materials Research (Volume 845)
• Pages: 41-45
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.845.41
• Online since: December 2013
• Authors: Kar Hoou Hui*, Lim Meng Hee, M. Salman Leong, Ahmed M. Abdelrhman
• Keywords: Decomposition, Fault Diagnosis, Machinery, Time-Frequency Analysis, Wavelet

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