Vibration sensor-based bearing fault diagnosis using ellipsoid-ARTMAP and differential evolution algorithms
| Autor: | Qinglu Xie, Guofeng Wang, Yanchao Zhang, Chang Liu |
|---|---|
| Rok vydání: | 2014 |
| Předmět: |
Engineering
Transducers lcsh:Chemical technology Biochemistry Vibration Cross-validation Article Analytical Chemistry Wavelet packet decomposition law.invention law fault classification rolling element bearing Ellipsoid-ARTMAP network differential evolution lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation Bearing (mechanical) business.industry Numerical Analysis Computer-Assisted Equipment Design Ellipsoid Atomic and Molecular Physics and Optics Equipment Failure Analysis Rolling-element bearing Differential evolution Decision boundary Equipment Failure business Algorithm Smoothing Algorithms |
| Zdroj: | Sensors (Basel, Switzerland) Sensors, Vol 14, Iss 6, Pp 10598-10618 (2014) Sensors; Volume 14; Issue 6; Pages: 10598-10618 |
| ISSN: | 1424-8220 |
| Popis: | Effective fault classification of rolling element bearings provides an important basis for ensuring safe operation of rotating machinery. In this paper, a novel vibration sensor-based fault diagnosis method using an Ellipsoid-ARTMAP network (EAM) and a differential evolution (DE) algorithm is proposed. The original features are firstly extracted from vibration signals based on wavelet packet decomposition. Then, a minimum-redundancy maximum-relevancy algorithm is introduced to select the most prominent features so as to decrease feature dimensions. Finally, a DE-based EAM (DE-EAM) classifier is constructed to realize the fault diagnosis. The major characteristic of EAM is that the sample distribution of each category is realized by using a hyper-ellipsoid node and smoothing operation algorithm. Therefore, it can depict the decision boundary of disperse samples accurately and effectively avoid over-fitting phenomena. To optimize EAM network parameters, the DE algorithm is presented and two objectives, including both classification accuracy and nodes number, are simultaneously introduced as the fitness functions. Meanwhile, an exponential criterion is proposed to realize final selection of the optimal parameters. To prove the effectiveness of the proposed method, the vibration signals of four types of rolling element bearings under different loads were collected. Moreover, to improve the robustness of the classifier evaluation, a two-fold cross validation scheme is adopted and the order of feature samples is randomly arranged ten times within each fold. The results show that DE-EAM classifier can recognize the fault categories of the rolling element bearings reliably and accurately. |
| Databáze: | OpenAIRE |
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