Research and Application of BSS Algorithm on The Gearbox Fault Diagnosis Based on The MMI Criterion
This paper presented a kind of blind source separation (BSS) technology and applied it into the gearbox fault diagnosis through the blind mixing signal separation. The algorithm based on the natural gradient fixed step-length was used to calculate the statistical independent source signal estimate value, and successfully extracted the fault information according to the separation signal power spectrum based on the minimum mutual information (MMI) criterion. The gearbox fault condition can be diagnosed effectively through the experiment proved, which provided a new method to the mechanical equipment fault diagnosis and running state monitor.
Keywords: BSS, MMI, natural gradient, mechanical equipment, fault diagnosis.
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ABOUT THE AUTHORS
Yu Chen
Yu Chen received the Master degree in data collection and signal processing from Northwestern Polytechnical University, in 2009. Currently, he is an Associate Professor at Zhengzhou Institute of Aeronautical Industry Management. His interests are in data collection and signal processing and nonlinear system modeling.
Haitao Jiang
HAITAO JIANG received the Master degree in Circuit and System professional from Northwestern Polytechnical University, in 2009. Currently, he is an Lecturer at Department of Physics and Electronics engineering, Jiaozuo Teachers College. His interests are in intelligent sensor data collection and signal processing.
Yu Chen
Yu Chen received the Master degree in data collection and signal processing from Northwestern Polytechnical University, in 2009. Currently, he is an Associate Professor at Zhengzhou Institute of Aeronautical Industry Management. His interests are in data collection and signal processing and nonlinear system modeling.
Haitao Jiang
HAITAO JIANG received the Master degree in Circuit and System professional from Northwestern Polytechnical University, in 2009. Currently, he is an Lecturer at Department of Physics and Electronics engineering, Jiaozuo Teachers College. His interests are in intelligent sensor data collection and signal processing.
