本研究利用希爾伯特-黃變換(Hilbert-Huang Transform)針對齒輪箱的定轉速實驗進行故障診斷，並以時頻分析來判斷故障嚴重程度的大小。針對齒輪箱常見的故障：斷齒、磨損以及質量不平衡等等，先將複雜訊號分解成若干個IMF，再以包絡線分析從中提取故障特徵，作為診斷的依據。 將時域訊號及HHT分析的結果中提取若干個特徵，利用主成份分析法將維度化簡，得到簡化後的綜合指標。將綜合指標當作類神經網路分類的輸入，分析的結果顯示，透過主成份降維後可以提高類神經網路的準確率。 In this study, Hilbert-Huang Transform (HHT) is utilized for fault diagnosis under fixed rotating speed. The time-frequency analysis is to identify the severity of the gear faults. The experimental cases include the common faults of the gearbox, such as broken teeth, gear wearing and gear unbalance. The complicated vibration signals due to faults are first decomposed into a number of Intrinsic Mode Functions (IMFs), and then the envelope analysis is employed to extract the fault characteristics. Specific features of time-domain signals as well as the results of HHT analysis are extracted for Principal Component Analysis (PCA) to achieve the characteristic dimension reduction. The composite indicators obtained from PCA are used as the inputs of Neural Network to classify the different gear faults. The analysis results show that through PCA, the characteristic dimension can be reduced and the classifying accuracy of neural network can be also improved.
