The research of object tracking filter fault diagnosis method based on reconstruction of feature vector in fractional Fourier transform domain

Abstract

To carry out state identification and fault diagnosis of mechanical equipment, a new method is proposed for fault diagnosis of mechanical equipment based on the reconstruction of feature vector in the fractional Fourier transform domain. First, the time series of vibration signals are performed by fractional Fourier transform in different orders, according to statistical characteristic in the fractional Fourier transform domain. The kurtosis coefficients are then used for searching the optimal order. Next, taking the normal signal as the tracking target, the difference between the detection signal and the normal signal in fractional Fourier transform domain is calculated in the optimal order, so the normal signal is filtered and the state vector is extracted. The characteristic parameters of the correlation dimension, the zero-order moment, and the kurtosis coefficient are then restructured as a feature vector, and finally, the states are recognized using the k-nearest neighbor cross-validation estimation method. The effect of feature extraction is analyzed by correlation coefficient and diagnostic effect is evaluated using the correct rate of diagnosis. Experiment and analysis illustrates that this proposed target tracking filter fault diagnosis method based on reconstruction of feature vector in fractional Fourier transform domain is simple and intuitive. This method has a favorable feature extraction effect, high accuracy rate of diagnosis, high state recognition efficiency, and good recognition stability, making it a viable option for quantitative real-time state recognition and fault diagnosis of mechanical vibration equipment.