Feature Extraction Of Colored Noise And Its Application In Motor Fault Diagnosis

The motor is the power source device of all large mechanical equipment. Inorder to ensure the normal production of industry and agriculture, the healthstatus of the motor need to be closely monitored to avoid the occurrence of thefault. When the motor is at fault, it will show all kinds of anomalies. Duringthese anomalies, noise signal is one of the more obvious characteristics.In this paper, in view of the difference of the noise emitted by the motor indifferent states, some characteristic values of different noise signal can be usedas the standard of the motor fault diagnosis. Analyzing and comparing noiseclassification, nature and a variety of feature extraction methods, the conclusioncan be drawn that any noises which exist in nature are colored noise and wavelettransform, especially wavelet packet transform, is an effective tool to extractcolored noise characteristics; doing some further analysis and study aboutpseudo white noise and pink noise, putting forward pseudo white noisewhitening model and pink noise ARMA model generation method, and using thecolored noise feature extraction method which is mentioned in this paper tosimulate these two models, so as to test their effect; according to these theoreticalresults obtained in the study process of colored noise, doing the research of themotor fault diagnosis.This paper designs a motor fault diagnosis system which combines signaldetection methods, signal analysis and feature extraction methods as well asneural network fault diagnosis technology. Its basic process is: using the signalacquisition module to detect the colored noise signal generated by the motor; byusing wavelet packet decomposition technology to analyze and extract acquiredsignals’ features; then making use of RBF neural network technology to identifyand judge the character of the extracted signal, and doing some decision-makingabout the motor running state.By using a medium-sized motor whose model is Y90S―4as the actual object, this fault diagnosis system is applied to detect and diagnosis severalcommon motor fault signals of the simulation setting in real time, and somerelated simulation experiments can be done. According to the simulation resultsof Matlab, it is shown that this system can achieve excellent effect. Comparingwith other fault diagnosis system, it has many advantages, such as fasterdiagnostic speed, higher accuracy, simpler structure, lower cost and so on.Therefore, it has better development prospect and broader market space and canbe greatly used into the practice of industrial and agricultural production.