CN103076563A

CN103076563A - Automatic diagnosis method of alternating-current asynchronous motor

Info

Publication number: CN103076563A
Application number: CN2012104410379A
Authority: CN
Inventors: 于星光
Original assignee: Kunshan Beiji Photoelectron Science & Technology Co Ltd
Current assignee: Kunshan Beiji Photoelectron Science & Technology Co Ltd
Priority date: 2012-11-07
Filing date: 2012-11-07
Publication date: 2013-05-01

Abstract

The invention discloses an automatic diagnosis method of an alternating-current asynchronous motor. The automatic diagnosis method comprises the following steps of: adopting an improved algorithm of wavelet packet decomposition and single-node reconstruction for detected signals, fining information characteristics of the signals and eliminating frequency-band confusion; adopting the tolerance range as an error judging standard, counting the average value, calculating energy vector values in all frequency bands and forcing the fault diagnosis error to be minimized; constructing normalized energy characteristic vectors, emphasizing fault characteristics and improving the real-time performance of diagnosis; and establishing a mapping relation between the fault characteristic vectors and correcting measures and correcting the rotating speed of the motor by adjusting a pulse-width modulation (PWM) wave. The automatic diagnosis method disclosed by the invention has the advantages that the error correcting performance and the speed adjusting accuracy are better, an effective method with characteristic extraction, fault diagnosis and automatic correction is achieved, and the loss of a steam turbine due to unplanned stop is reduced.

Description

A kind of AC induction motor automatic diagnosis method

Technical field

The invention belongs to Steam Turbine automatic diagnosis field, relate in particular to a kind of AC induction motor automatic diagnosis method.

Background technology

Vibration is an important technology index that is directly connected to the normal operation of Turbo-generator Set.Since self generating sets came out, analysis of vibration measurement, fault diagnosis and treatment technology just produced thereupon.In nearly one, 20 year, along with the fast development of power industry, the following features relevant with fault diagnosis occurred: unit maximizes increasingly, complicated, and automaticity improves day by day; Modern electric is produced the reliability of equipment is had higher requirement, and the raising of unit parameter and the increase of capacity are so that because also thereupon exponentially increase of the economic loss that the unit non-programmed halt that the shafting vibration defective causes brings.These all require diagnostic techniques to develop rapidly, to be complementary with production status more than all.

Summary of the invention

The object of the present invention is to provide a kind of AC induction motor automatic diagnosis method.

The technical scheme that realizes above-mentioned purpose is: a kind of AC induction motor automatic diagnosis method may further comprise the steps:

S1, to detected signal sampling, carry out WAVELET PACKET DECOMPOSITION, if detected voltage signal is U (t), prefilter by correspondence, discrete signal after the analog to digital conversion is U (T), signal U (T) very intactly is divided into detected voltage fault signal in the different frequency ranges through small echo conjugate quadrature mirror mirror filter, realizes the WAVELET PACKET DECOMPOSITION of fault.

S2, use improve algorithm, eliminate frequency aliasing, in order to solve frequency interleave in the subband and the false frequency component in each subband, avoid frequency alias, adopt the improvement algorithm of WAVELET PACKET DECOMPOSITION and single node reconstruct, namely draw again two operators in front the basis.

S3, ask the energy eigenvalue in each subband, tentatively set up the fault the feature parameter vectors, for convenient and judge exactly fault type, calculate and decompose the afterwards energy of each in-band signal.Make up fault feature vector T with the energy element in each subband, with the signal message of the feature parameter vectors faults at time domain and frequency domain.

S4, to sample space statistical average, precision energy proper vector value in order to reduce error, has carried out repeatedly measuring when setting up fault feature vector, sets up the test figure sample of fault, and to its statistical average.

The normalized energy proper vector of S5, transformation fault for the ease of data analysis, improves arithmetic speed and accuracy, carries out the normalization reforming processing again, and through processing, the signal fault feature is more obvious.Because fault signature is compressed from the higher-dimension to the lower dimensional space, set up normalized energy proper vector ET, reduced the input node, reduced computation complexity, accelerated speed of convergence, improved real-time and the accuracy rate of diagnosis.

The foundation of S6, fault dictionary, determine the one by one mapping relations of motor most common failure and the feature parameter vectors and automatic correction measure, this corresponding relation is made in the storer that the fault inquiry dictionary is stored in the single-chip microcomputer peripheral hardware, so that identification fault in the middle of motor and controller operation, show diagnostic result, realize automatic calibration.

S7, adjusting are revised, and correspondence is fault dictionary and the correcting scheme of foundation in advance, export the break-make of PWM ripple control thyristor by the PWM corrective network, regulate the voltage that is added on the motor winding, guarantee stability and the accuracy exported.

This method has preferably error-correcting performance and adjusting speed accuracy, is feature extraction, fault diagnosis and the automatic effective ways of revising, and reduces steam turbine because the loss that non-programmed halt brings.

Description of drawings:

Fig. 1 is schematic flow sheet of the present invention.

Embodiment

The following describes preferred implementation strategy of the present invention.

The present embodiment AC induction motor automatic diagnosis method specific embodiments is as follows:

Below in conjunction with the embodiments the present invention is had been described in detail, those skilled in the art can make the many variations example to the present invention according to the above description.Thereby some details among the embodiment should not consist of limitation of the invention, and the scope that the present invention will define with appended claims is as protection scope of the present invention.

Claims

1. an AC induction motor automatic diagnosis method is characterized in that, may further comprise the steps:

S1, to detected signal sampling, carry out WAVELET PACKET DECOMPOSITION, if detected voltage signal is U (t), prefilter by correspondence, discrete signal after the analog to digital conversion is U (T), signal U (T) very intactly is divided into detected voltage fault signal in the different frequency ranges through small echo conjugate quadrature mirror mirror filter, realizes the WAVELET PACKET DECOMPOSITION of fault;

S2, use improve algorithm, eliminate frequency aliasing, in order to solve frequency interleave in the subband and the false frequency component in each subband, avoid frequency alias, adopt the improvement algorithm of WAVELET PACKET DECOMPOSITION and single node reconstruct, namely draw again two operators in front the basis;

S3, ask the energy eigenvalue in each subband, tentatively set up the fault the feature parameter vectors, for convenient and judge exactly fault type, calculate and decompose the afterwards energy of each in-band signal.Make up fault feature vector T with the energy element in each subband, with the signal message of the feature parameter vectors faults at time domain and frequency domain;

S4, to sample space statistical average, precision energy proper vector value in order to reduce error, has carried out repeatedly measuring when setting up fault feature vector, sets up the test figure sample of fault, and to its statistical average;

The normalized energy proper vector of S5, transformation fault, for the ease of data analysis, improve arithmetic speed and accuracy, carry out again the normalization reforming processing, through processing, the signal fault feature is more obvious, because fault signature is compressed from the higher-dimension to the lower dimensional space, has set up normalized energy proper vector ET, reduced the input node, reduce computation complexity, accelerated speed of convergence, improved real-time and the accuracy rate of diagnosis;

The foundation of S6, fault dictionary, determine the one by one mapping relations of motor most common failure and the feature parameter vectors and automatic correction measure, this corresponding relation is made in the storer that the fault inquiry dictionary is stored in the single-chip microcomputer peripheral hardware, so that identification fault in the middle of motor and controller operation, show diagnostic result, realize automatic calibration;