Multi-fault clustering and diagnosis of gear system mined by spectrum entropy clustering based on higher order cumulants

Higher order cumulants (HOC) is a new kind of modern signal analysis of theory and technology. Spectrum entropy clustering (SEC) is a data mining method of statistics, extracting useful characteristics from a mass of nonlinear and non-stationary data. Following a discussion on the characteristics of HOC theory and SEC method in this paper, the study of signal processing techniques and the unique merits of nonlinear coupling characteristic analysis in processing random and non-stationary signals are introduced. Also, a new clustering analysis and diagnosis method is proposed for detecting multi-damage on gear by introducing the combination of HOC and SEC into the damage-detection and diagnosis of the gear system. The noise is restrained by HOC and by extracting coupling features and separating the characteristic signal at different speeds and frequency bands. Under such circumstances, the weak signal characteristics in the system are emphasized and the characteristic of multi-fault is extracted. Adopting a data-mining method of SEC conducts an analysis and diagnosis at various running states, such as the speed of 300 wr/min, 900 r/min, 1200 r/min, and 1500 r/min of the following six signals: no-fault, short crack-fault in tooth root, long crack-fault in tooth root, short crack-fault in pitch circle, long crack-fault in pitch circle, and wear-fault on tooth. Research shows that this combined method of detection and diagnosis can also identify the degree of damage of some faults. On this basis, the virtual instrument of the gear system which detects damage and diagnoses faults is developed by combining with advantages of MATLAB and VC++, employing component object module technology, adopting mixed programming methods, and calling the program transformed from an *. m file under VC++. This software system possesses functions of collecting and introducing vibration signals of gear, analyzing and processing signals, extracting features, visualizing graphics, detecting and diagnosing faults, detecting and monitoring, etc. Finally, the results of testing and verifying show that the developed system can effectively be used to detect and diagnose faults in an actual operating gear transmission system. (C) 2013 American Institute of Physics. [http://dx.doi.org/10.1063/1.4789777]