Abstract
This paper proposes a novel fault feature extraction method with the aim of extracting the fault feature submerged in the single-channel observation signal. The proposed method integrates the strengths of the constrained independent component analysis (cICA) extracting only the signals of interest (SOIs) with the advantage of ensemble empirical mode decomposition (EEMD) alleviating the mode mixing. The method, which is named EEMD-based cICA, not only enables gear fault feature extraction but also offers a new independent component analysis (ICA) mixing model with source noise and measured noise for the single-channel observation signal. The efficiency of the proposed method is tested on simulated as well as real-world vibration signals acquired from a multi-stage gearbox with a missing tooth and a chipped tooth, respectively.
Highlights
The goal of independent component analysis (ICA) [1,2,3,4] is to recover all the source signals from mixed signals at a time
There are many problems to be solved for ICA applications: (1) classical ICA algorithm has some ambiguities, such as unknown number of source signals, undetermined the variance and the order of the independent components (ICs); (2) ICA model does not consider the source noise and measured noise simultaneously [3]; (3) It is desired to extract only the signals of interest (SOIs). (4) The difficulty of the single-channel observation signal signature extraction based on ICA, it belongs to the extreme case of the underdetermined BBS problem [4]
We developed an ensemble empirical mode decomposition (EEMD)-based constrained independent component analysis (cICA) method to separate fault signal from the single-channel observation signal ( )
Summary
The goal of independent component analysis (ICA) [1,2,3,4] is to recover all the source signals from mixed signals at a time. EEMD-BASED CICA METHOD FOR SINGLE-CHANNEL SIGNAL SEPARATION AND FAULT FEATURE EXTRACTION OF GEARBOX. In the extreme underdetermined BBS case, that is to say, single-channel observation signal separation, the number of sensor is only one This is a very undesirable requirement for real-world applications because the number of active source signals is unknown in advance in most practical situations. In this case, single-channel observation signal mixing matrix is not invertible, and the traditional ICA or cICA methods fail to recover all sources, which leads to the result that the desired signal cannot be extracted directly from the single-channel observation signal.
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