Affiliations: [a] Department of Architecture and Civil Engineering, Chongqing Telecommunication Polytechnic College, Chongqing, China
| [b] School of Management, Chongqing Jiaotong University, Chongqing, China
Correspondence:
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Corresponding author. Xuejiao Li, Department of Architecture and Civil Engineering, Chongqing Telecommunication Polytechnic College, Chongqing 402247, China. [email protected].
Abstract: Reliable degradation prognosis of mechanical components is very important for condition-based maintenance to improve the reliability and reduce the cost of maintenance. This paper reports the development of a fuzzy feature fusion and multimodal regression method for the degradation prognosis of mechanical components. Initially, the raw features from the vibration signals of the mechanical components are extracted. A degradation index is subsequently yielded by merging the obtained features through/using the fuzzy fusion technique. The ensemble empirical mode decomposition is then introduced to decompose the fusion index into several multimodal sub-series to acquire more detailed information. Extreme learning machines are established to predict the sub-series in different modes. The predicted results are obtained by integrating the multimodal sub-results. The reported approach was evaluated with real data from a rolling element bearing. Moreover, two peer models were imported to validate the effectiveness of the proposed method. The experimental results indicate that the reported approach is capable of erecting the degradation index reflecting the bearing degradation and that it had better performance in the remaining useful life prediction than the peer methods.
