Fault Detection Method for Electric Opening Valves Based on Wavelet Packet Analysis and Optimized KNN
To address the issue of difficult integration of a highly efficient and low-computational-fault detection subsystem into an electric opening valve control system with a microcontroller unit(MCU)as its control core,this paper propose a fault detection method for electric opening valves based on wavelet packet transform and optimized K-Nearest Neighbor(KNN)algorithm.The wavelet packet transform is applied to the valve vibration signal,and the energy values of the wavelet nodes are calculated together with the time domain characteristics of the reconstructed signal.Based on the Pearson coefficients,two fault characteristics parameters with strong energy correlation:peak-to-peak value and root mean square,and both are used as sample evaluation indicators for the KNN algorithm;the distance calculation formula of the KNN algorithm is optimized by weighting the evaluation indicators,and fault detection tests are conducted in MATLAB and the experimental prototype respectively,with corresponding highest classification accuracy rates of 92.5％and 86.7％.The results show that experimental test and simulation analysis have good consistency,and the advantage of this fault detection method is that it has small amount of calculation,high fault identification rate,and can be effectively applied to the electric opening valve control system with MCU as the core.
electric opening valvewavelet packet transformoptimization KNNfault detection
万方数据
维普
