Mine Ventilation Fan Status Monitor Distortion Signal Identification And Analysis

The normal operation of mine ventilator is directly related to the stability and efficiency of coal mine system.This topic selection of mine ventilator as an object of monitoring and fault diagnosis analysis when the fan is first occurrence rotor unbalance of the rotor,rotor system the common failures such as wear and bearing wear,ventilator parameters such as temperature,power,air volume and air pressure does not meet the threshold requirements,but in the operation of the actual parameter trend and time series prediction model is formed through a large amount of data than the distortion of the original online monitoring system of condition does not trigger the early warning system,so in order to avoid misses a chance to found the early malfunction of the ventilator,using AIC9900 diagnostic instrument fan vibration signal acquisition and equipment failure analysis by wavelet packet analysis technology for multifrequency vibration signals separation,will eventually energy eigenvector of frequency and classic table corresponding to the fault features,the play will mine ventilator distortion signal and failure form of the corresponding function.This paper adopts the method of time series analysis can be used to create a large number of original data in mathematical model of integration,through the fan of the existing data to predict the next time data,to find the parameters of the ventilator doing basic distortion signal.Wavelet packet transform is a kind of signal time-scale()time-frequency analysis method,it can overcome the Fourier transform on the local unsteady signal defects,will unify the distortion signal of time domain and frequency domain analysis and have the characteristics of multi-resolution analysis.In this paper,based on time series analysis method of integrated application of wavelet packet feature extraction technology of distortion signal of time-frequency signal analysis,put forward a set of complete distortion signal and which corresponds to the number of fault diagnosis system.