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Lee, Seungchul
iSystems Design Lab
Research Interests
  • Intelligent design for products and manufacturing systems


Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis

DC Field Value Language Cui, Lingli ko Wang, Jing ko Lee, Seungchul ko 2015-01-06T00:03:25Z - 2015-01-05 ko 2014-05 -
dc.identifier.citation JOURNAL OF SOUND AND VIBRATION, v.333, no.10, pp.2840 - 2862 ko
dc.identifier.issn 0022-460X ko
dc.identifier.uri -
dc.identifier.uri ko
dc.description.abstract The sparse decomposition based on matching pursuit is an adaptive sparse expression of the signals. An adaptive matching pursuit algorithm that uses an impulse dictionary is introduced in this article for rolling bearing vibration signal processing and fault diagnosis. First, a new dictionary model is established according to the characteristics and mechanism of rolling bearing faults. The new model incorporates the rotational speed of the bearing,, the dimensions of the bearing and the bearing fault status, among other parameters. The model can simulate the impulse experienced by the bearing at different bearing fault levels. A simulation experiment suggests that a new impulse dictionary used in a matching pursuit algorithm combined with a genetic algorithm has a more accurate effect on bearing fault diagnosis than using a traditional impulse dictionary. However, those two methods have some weak points, namely, poor stability, rapidity and controllability. Each key parameter in the dictionary model and its influence on the analysis results are systematically studied, and the impulse location is determined as the primary model parameter. The adaptive impulse dictionary is established by changing characteristic parameters progressively. The dictionary built by this method has a lower redundancy and a higher relevance between each dictionary atom and the analyzed vibration signal. The matching pursuit algorithm of an adaptive impulse dictionary is adopted to analyze the simulated signals. The results indicate that the characteristic fault components could be accurately extracted from the noisy simulation fault signals by this algorithm, and the result exhibited a higher efficiency in addition to an improved stability, rapidity and controllability when compared with a matching pursuit approach that was based on a genetic algorithm. We experimentally analyze the early-stage fault signals and composite fault signals of the bearing. The results further demonstrate the effectiveness and superiority of the matching pursuit algorithm that uses the adaptive impulse dictionary. Finally, this algorithm is applied to the analysis of engineering data, and good results are achieved. ko
dc.description.statementofresponsibility close -
dc.language ENG ko
dc.subject WAVELET ko
dc.subject TRANSFORM ko
dc.subject SIGNALS ko
dc.title Matching pursuit of an adaptive impulse dictionary for bearing fault diagnosis ko
dc.type ARTICLE ko
dc.identifier.scopusid 2-s2.0-84894107471 ko
dc.identifier.wosid 000333504500009 ko
dc.type.rims ART ko
dc.description.wostc 9 *
dc.description.scopustc 6 * 2015-12-28 * 2015-11-04 *
dc.identifier.doi 10.1016/j.jsv.2013.12.029 ko
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