On-line monitoring and diagnosis method of AC (alternating current) motor based on statistical model

Abstract

The invention relates to a monitoring and diagnosis method of an AC (alternating current) motor, in particular to an on-line monitoring and diagnosis method of an AC motor based on a statistical model. The method comprises the following steps: signal acquisition, status self-learning, model establishment, and monitoring and diagnosis. In the method, by identifying a set of status characteristic quantities related to the electrical and mechanical faults of a motor system, the statistical model is established by a multivariate statistical method, and the alarm limit in the even that the model is deviated from the normal range and the contribution of variables are determined by a self-learning mode so as to early warn the defects of the motor system. The on-line monitoring and diagnosis method of the AC motor has the advantages that comprehensive diagnosis can be carried out on the problems of the motor system based on the statistical model; the voltage and current signals of the motor can be obtained in an electrical room only by a mutual inductor without sensor installation and signal transmission in the production field; by adopting a warning control line in the self-learning step, the influence of system errors on judgment is inhibited; and the defects on on-line monitoring and current-method diagnosis of the existing motor system are compensated, and on-line monitoring and abnormal locating functions are realized.

Description

technical field [0001] The invention relates to monitoring and diagnosis of an AC motor system, in particular to a method for on-line monitoring and diagnosis of an AC motor. Background technique [0002] At present, the diagnosis of AC motors is mainly based on the MCSA method. This method is based on the frequency spectrum analysis of the current, so it is carried out in an offline analysis mode in most cases. In recent years, scientific researchers have continuously introduced more complex signal analysis methods into this field, such as cyclostationary methods, wavelet analysis methods, singular value decomposition methods, etc., which have increased the accuracy of analysis, and also increased the difficulty of software analysis and the impact on system resources. It is not conducive to the realization of online diagnosis of the motor system. However, the method of eliminating 50Hz power frequency components by hardware means is difficult to perform in the high electr...

AI Technical Summary

Problems solved by technology

However, for the monitoring of the motor system, it is almost impossible to obtain a unified alarm rule due to the ever-changing working parameters and working conditions of the motor system in field service.

For the special equipment that provides power, the motor is not allowed to gradually deteriorate like other mechanical equipment, and it is often dealt with and replaced if there is a slight problem, which makes it too late for diagnostic researchers to capture complete characteristics. Access to diagnostic features and rules is also elusive

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