應用約束獨立成分分析和經驗模態分解法來診斷同步發生之多重軸承故障

Application of Constrained Independent Component Analysis and Empirical Mode Decomposition to Diagnose Synchronous Multiple Bearing Faults

呂霽軒(Chi-Hsuan Lu) ； 陳彥翔(Yan-Siang Chen) ； 吳天堯(Tian-Yau Wu)

《應用聲學與振動學刊》 11卷1&2期 (2019/12) Pp. 9-17

https://doi.org/10.6282/JASV.201912_11(1_2).0002

摘要

本研究透過經驗模態分解法和約束獨立成分分析來診斷軸承同時發生之複合故障的型態。首先透過經驗模態分解法將訊號拆解成若干個固有模態函數，再選取具明顯振幅調制現象的固有模態函數合成新訊號，透過約束獨立成分分析來找出獨立成分，接下來將經過約束獨立成分分析的獨立成分透過快速傅立葉變換來取得含有故障訊號的頻域特徵，並與理論特徵值進行對照。最後量化此獨立成分之時域與頻域特徵，利用支持向量機來進行智慧型故障類型之識別。

關鍵字

約束獨立成分分析 ； 經驗模態分解法 ； 同時複合故障 ； 支持向量機

並列摘要

This study investigates the diagnosis of multiple faults that occur concurrently in the bearing through empirical mode decomposition and constrained independent component analysis. The vibration measurements are first decomposed into several intrinsic modal functions through the empirical mode decomposition method. The intrinsic mode functions that present obvious amplitude modulation phenomenon are selected to synthesize a new signal. The constrained independent component analysis is employed to extract the signal component which is highly correlated to the bearing fault features. The fast Fourier transform is utilized to obtain the frequency-domain features of the faulted signal, and the extracted features are compared with the ones derived from the theoretical characteristics. The time-domain and frequency-domain characteristics of this independent component are quantified for the intelligent diagnosis through the support vector machine classifier.

並列關鍵字

Constrained Independent Component Analysis ； Empirical Mode Decomposition ； Concurrent Multiple Bearing Fault ； Support Vector Machine