結合經驗模態分解與多尺度熵分析之階次追蹤技術於非固定轉速之軸承故障診斷;Application of Empirical Mode Decomposition and Multi-scale Entropy Analysis to the Roller Bearing Fault Diagnosis under Variable Rotation Speed via Order Tracking Technology

NCUIR > Engineering College > Executive Master of Mechanical Engineering > Electronic Thesis & Dissertation > Item 987654321/53807

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/53807

Title:	結合經驗模態分解與多尺度熵分析之階次追蹤技術於非固定轉速之軸承故障診斷;Application of Empirical Mode Decomposition and Multi-scale Entropy Analysis to the Roller Bearing Fault Diagnosis under Variable Rotation Speed via Order Tracking Technology
Authors:	余長霖;Yu,Chang-Lin
Contributors:	機械工程研究所碩士在職專班
Keywords:	軸承故障診斷;階次追蹤;多尺度熵;決策樹;變轉速;經驗模態分解法;Multi-scale entropy;variable rotation speeds;Empirical Mode Decomposition;decision tree;bearing fault diagnosis;order tracking
Date:	2012-07-02
Issue Date:	2012-09-11 18:16:44 (UTC+8)
Publisher:	國立中央大學
Abstract:	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.
Appears in Collections:	[Executive Master of Mechanical Engineering] Electronic Thesis & Dissertation

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