We present a generic methodology for machinery fault diagnosis through pattern recognition techniques. The proposed method has the advantage of dealing with complicated signatures, such as those present in the vibration signals of rolling element bearings with and without defects. The signature varies with the location and severity of bearing defects, load and speed of the shaft, and different bearing housing structures. More specifically, the proposed technique contains effective feature extraction, good learning ability, reliable feature fusion, and a simple classification algorithm. Examples with experimental testing data were used to illustrate the idea and effectiveness of the proposed method.
Pattern Recognition for Automatic Machinery Fault Diagnosis
Contributed by the Technical Committee on Vibration and Sound for publication in the JOURNAL OF VIBRATION AND ACOUSTICS. Manuscript received December 2002; Revised April 2003. Associate Editor: M. I. Friswell.
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Sun, Q., Chen , P., Zhang, D., and Xi, F. (May 4, 2004). "Pattern Recognition for Automatic Machinery Fault Diagnosis ." ASME. J. Vib. Acoust. April 2004; 126(2): 307–316. https://doi.org/10.1115/1.1687391
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