小波包分形技術結合類神經網路在旋轉機械故障診斷之研究

Faults Diagnosis of Rotating Machinery Using Wavelet Packets-Fractal Technology in Combination with Neural Network

本研究提出以小波包分形技術結合類神經網路的方法，形成一套新的故障診斷模式。其目的是探討旋轉機械在轉動不平衡、偏心、基座鬆動及綜合性等不同類型故障發生時，所產生的非穩態振動訊息。以小波包分形技術作為訊號的故障特徵識別工具，將訊號分解成各自獨立的頻帶，然後計算每個頻帶訊號的盒維數，以盒維數量化的結果來描述系統之狀況。並探討不同故障狀況下盒維數的變化，並結合徑向類神經網路來達到故障模式的識別。從實驗的結果證明本方法具有良好的故障診斷辨識能力，為旋轉機械故障診斷提供了另一種診斷方法。 This paper presents a new fault diagnosis procedure for rotating machinery based on wavelet packets-fractal technology in combination with neural network. The main purpose is to investigate different fault mechanism in rotating machinery, such as imbalance, misalignment, or imbalance with misalignment conditions, etc. When these faults occur they usually produce nonstationary vibration signals, by using wavelet packets transform on these signals, the fractal dimension of each frequency channel is extracted and the box dimension is used to depict the failure characteristics of vibration signals, and then the failure modes can be classified by radial basis function neural network. Experiments were conducted and the results shown that the proposed method can detect and recognize different kinds of faults in rotating machinery. Therefore, it is concluded that the wavelet packets-fractal technology combined with neural network method can provide an effective way to diagnosis faults in mechanical systems.