Abstract
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.
Highlights
As a fundamental way of characterizing the condition of a mechanical system, diagnostics should fulfill the requirement of being able to detect all possible faults
Are used alone as condition indicators, and their performance complies with the expectations perfectly, but in this paper, the signal we use is an acoustic signal which has quite a lot differences compared with vibrations, so the 10-SF are used alone as input condition indicators to be sent to pairwise-coupled relevance vector machine (PCRVM) as a comparison
To verify the effectiveness of the proposed denoising technique, denoised acoustic signals based on PCRVM are used to compare with the original signals
Summary
As a fundamental way of characterizing the condition of a mechanical system, diagnostics should fulfill the requirement of being able to detect all possible faults. As a defective component may run for certain time before it is totally damaged, the early detection of machinery fault and making corresponding maintenance arrangements have great impact on the reduction of unexpected shutdown and operation cost. The condition monitoring process includes the acquisition of information, signal processing and pattern recognition. Various signal types, including vibrations, acoustic signals, and temperature, have been used for fault diagnosis. Considering the advantages of acoustic signals, there has been a tendency to apply acoustic signal analysis to machinery fault detection and diagnosis [1]. Acoustic signals are non-directional which means that one acoustic sensor can satisfy the data collection requirements, while signals on three axes are needed to be considered if using a vibration sensor
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