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Motor Fault Diagnosis Based on AR Parameter Model Spectrum Estimation

Lijun Wang, Shenfeng Zhang, Huijuan Guo


In order to diagnose the motor fault accurately, a new diagnosis method of motor fault was proposed which is based on AR parameter model spectrum estimation theory. As to real-time acquisition motor signals, energy signals were firstly got from acquisition signals and then were extracted from new samples at 10 sampling interval, and finally AR parameter model spectrum estimation was done on the small sample signals, when the data were relatively short, AR parameter model spectrum estimation can also present signals contained all frequency components. Based on the different frequency characteristics of motor signals on the power spectra, the diagnosis cause of motor signals can be determined. The implementation of the theory can well describe the power spectrum diagram of the signals which has the characteristics of smooth spectral lines, sharp spectral peak and accurate frequency location; it also can improve the resolution of spectrum estimation and facticity.


AR parameter model, power spectral estimation, small sample signal, motor fault.

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