A new method of bearing fault diagnostics in complex rotating machines using multi-sensor mixtured hidden Markov models

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Z. S. Chen Y. M. Yang Z. Hu Z. X. Ge

Abstract

Vibration signals from complex rotating machines are often non-Gaussian and non-stationary, so it is difficult to accurately detect faults of a bearing inside using a single sensor. This paper introduces a new bearing fault diagnostics scheme in complex rotating machines using multi-sensor mixtured hidden Markov model (MSMHMM) of vibration signals. Vibration signals of each sensor will be considered as the mixture of nonGaussian sources, which can depict non-Gaussian observation sequences well. Then its parameter learning procedure is given in detail based on EM algorithm. In the end the new method was tested with experimental data collected from a helicopter gearbox and the results are very exciting.

How to Cite

Chen, Z. S., Yang, Y. M., Hu, Z., & X. Ge, Z. (2011). A new method of bearing fault diagnostics in complex rotating machines using multi-sensor mixtured hidden Markov models. Annual Conference of the PHM Society, 3(1). https://doi.org/10.36001/phmconf.2011.v3i1.1965
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Keywords

Bearings, Fault diagnostics, non-Gaussian, Multi-sensor, MSMHMM

References
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Section
Poster Presentations