Asynchronous motor fault monitoring and diagnosing method based on model

Abstract

The invention discloses an asynchronous motor fault monitoring and diagnosing method based on a model. The method comprises the following steps: firstly, acquiring a three-phase input voltage signal and a three-phase output current signal of an asynchronous motor which can be normally operated, establishing a mathematical model to serve as a fault-free model; carrying out parallel running on the fault-free model under driving of a same input voltage u to obtain a residual signal d; then carrying out time domain analysis on the residual signal d, determining a threshold value eta of the residual signal of the asynchronous motor according to the 3 sigma principle, and judging whether a fault occurs or not by monitoring whether a residual effective value dRMS exceeds the threshold value eta or not when the asynchronous motor is stably operated; carrying out frequency domain analysis on the residual signal d again, and determining the fault type according to a fault feature frequency component fF appeared in a residual spectrum. The monitoring and diagnosing method disclosed by the invention can effectively weaken adverse effects on motor fault monitoring and diagnosis by an input voltage and improve the signal-to-noise ratio of a fault signal, thereby improving the sensitivity of the motor fault monitoring and the reliability of the fault diagnosis.

Description

Technical field [0001] The invention belongs to the technical field of motor fault detection, and relates to a step motor fault monitoring and diagnosis method, in particular to a model-based asynchronous motor fault monitoring and diagnosis method. Background technique [0002] The asynchronous motor has the characteristics of simple structure and reliable operation, and is the most widely used driving device in industrial and agricultural production. However, once the motor fails, the production process will be interrupted, causing serious economic losses, and even threatening the safety of on-site operators. [0003] At present, the fault diagnosis of the motor is mainly realized by directly analyzing the frequency spectrum of the stator current signal. When the motor fails, the following characteristic frequency components (k=1,2,...; n=1,3,5,...) will appear in the frequency spectrum of the stator current signal: [0004] Feature frequency of broken rotor bars: f r =(1±2ks)f 1...

AI Technical Summary

Problems solved by technology

[0012] However, the early faults of asynchronous motors show that the fault characteristics different from the normal state of the motor are usually very weak, and there are a large number of harmonic components in the motor input voltage signal, which will generate many additional interference frequencies in the current spectrum. Affects the diagnosis of motor faults, affects the sensitivity of motor fault monitoring and the reliability of fault diagnosis

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