Machinery Fault Detection Using the Wavelet Order Spectrum

C.C. Wang; Y. Kang; T.Y. Tu

doi:10.4028/www.scientific.net/AMR.488-489.1631

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 488-489Machinery Fault Detection Using the Wavelet Order...

Machinery Fault Detection Using the Wavelet Order Spectrum

Abstract:

The non-stationary signal features are averaged in correspondence with the length of analysis time, thus making it impossible to highlight the signal characteristics and caused the difficulties in identifying or diagnosing faults. In this paper, the wavelet order spectrum method using a combination of wavelet transform (WT) and speed frequency ordering. The feature order does not change with variations in speed, thus can effectively identify non-stationary faults in mechanical equipment. In addition, principal components analysis (PCA) is used to extract the main features of the wavelet order spectrum and reduce the volume of data. This is combined with self-organizing maps (SOM) to devise an artificial intelligence method for fault diagnosis in non-stationary states.

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Periodical:

Advanced Materials Research (Volumes 488-489)

Pages:

1631-1635

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.488-489.1631

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Online since:

March 2012

Authors:

C.C. Wang, Y. Kang, T.Y. Tu

Keywords:

Non-Stationary Signal, Self-Organizing Maps, Wavelet Order Spectrum

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