本論文結合了希爾伯特-黃轉換與多尺度熵的分析方法，在變轉速情況下，對旋轉機械軸承系統發生內圈損壞、外圈損壞、滾柱損壞等情況進行故障診斷。首先，利用階次追蹤方法將非穩態非線性的訊號轉換成穩態的角度域訊號，再藉由希爾伯特-黃轉換的經驗模態分解法拆解，將複雜訊號分解成若干個固有模態函數，對發生振幅調制現象的固有模態函數進行包絡，利用粗粒化的過程，將訊號轉換成新的尺度序列，對各尺度序列進行取樣熵的計算，從各尺度的取樣熵值提取故障特徵，最後利用決策樹辨別出各種故障類型，並建立其樹狀辨識模型。

In this paper, the novel approach combining Hilbert-Huang Transform (HHT) and the multi-scale entropy (MSE) analysis is utilized for diagnosing the roller bearing faults, such as inner race defect, outer race defect and roller defect, under the operating conditions of variable rotation speeds. The vibration signals are first measured through the order tracking technique, so that the signals are sampled with identical angle increment and thus the vibration signals are stationary without the factor of shaft rotation speed. The vibration signals are then decomposed into a number of Intrinsic Mode Functions (IMFs) by using the Empirical Mode Decomposition (EMD) method. The envelope analysis is employed to the IMFs that have amplitude modulation phenomenon. The envelope signals are transformed to the series of different scales by course-grained process and MSE of the series can be calculated. With the extracted features of the MSEs, the decision tree algorithm is utilized to classify the different faulted bearing types and faulted levels.

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