A Hybrid EEMD-Based SampEn and SVD for Acoustic Signal Processing and Fault Diagnosis

• Published in: Entropy (Basel, Switzerland) Vol. 18; no. 4; p. 112
• Main Authors: Yang, Zhi-Xin, Zhong, Jian-Hua
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.04.2016
• Subjects: acoustic signal processing; Computational efficiency; ensemble empirical mode decomposition (EEMD); Entropy; Failure; Fault diagnosis; Feature extraction; hybrid system; sample entropy (SampEn); Signal processing; Singular value decomposition; singular value decomposition (SVD); Statistical methods
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e18040112

Acoustic signals are an ideal source of diagnosis data thanks to their intrinsic non-directional coverage, sensitivity to incipient defects, and insensitivity to structural resonance characteristics. However this makes prevailing signal de-nosing and feature extraction methods suffer from high computational cost, low signal to noise ratio (S/N), and difficulty to extract the compound acoustic emissions for various failure types. To address these challenges, we propose a hybrid signal processing technique to depict the embedded signal using generally effective features. The ensemble empirical mode decomposition (EEMD) is adopted as the fundamental pre-processor, which is integrated with the sample entropy (SampEn), singular value decomposition (SVD), and statistic feature processing (SFP) methods. The SampEn and SVD are identified as the condition indicators for periodical and irregular signals, respectively. Moreover, such a hybrid module is self-adaptive and robust to different signals, which ensures the generality of its performance. The hybrid signal processor is further integrated with a probabilistic classifier, pairwise-coupled relevance vector machine (PCRVM), to construct a new fault diagnosis system. Experimental verifications for industrial equipment show that the proposed diagnostic system is superior to prior methods in computational efficiency and the capability of simultaneously processing non-stationary and nonlinear condition monitoring signals.