基于SPA-FIG与优化ELM的滚动轴承性能退化趋势预测

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摘要为了提高滚动轴承性能退化指标的预测精度，得到性能退化指标准确的预测范围，本文提出基于分解-模糊粒化与优化极限学习机（Extreme Learning Machine，ELM）的轴承性能退化趋势模糊粒化预测。首先利用平滑先验分析提取轴承性能退化指标序列的趋势项及波动项，再利用信息粒化方法对波动项进行模糊信息粒化；然后将趋势项及粒化后的波动项数据输入至ELM进行回归预测，并采用粒子群算法优化ELM参数；最后根据实测值和预测值的对比分析评估预测模型的优良性。实验结果表明，该方法可以有效跟踪轴承性能退化指标的变化趋势，并对其指标的波动范围进行有效预测。

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	陈强强1
	2
	戴邵武1
	戴洪德3
	朱敏1
	孙玉玉4

关键词 ：滚动轴承, 趋势预测, 模糊信息粒化, 极限学习机, 平滑先验分析

Abstract：In order to improve prediction accuracy of rolling bearing performance degradation index, and obtain an accurate prediction range of performance degradation index, a novel performance degradation trend prediction method based on smooth prior analysis (SPA)-fuzzy information granulation (FID) and optimized extreme learning machine (ELM) was proposed.Firstly, SPA was used to extract the trend term and fluctuation term of bearing performance degradation index sequences, and FIG was used to do fuzzy information granulation for the fluctuation term.Then, the trend term and the granulated fluctuationtermwere input into ELM to perform regression prediction, and the particle swarm optimization (PSO) was used to optimize ELM parameters. Finally, the excellence of the prediction model was estimated according to the contrastive analysis of the measured values and the predicted ones. Test results showed that the proposed method can effectively track the change trend of bearing performance degradation index and predict the fluctuation range of the index.

Key words： rolling bearing trend prediction fuzzy information granulation(FIG) extremelearning machine (ELM) smooth prior analysis (SPA)

收稿日期: 2019-06-28 出版日期: 2020-09-28

引用本文:

陈强强1,2，戴邵武1，戴洪德3，朱敏1，孙玉玉4. 基于SPA-FIG与优化ELM的滚动轴承性能退化趋势预测[J]. 振动与冲击, 2020, 39(19): 187-194.
CHEN Qiangqiang1,2, DAI Shaowu1, DAI Hongde3, ZHU Min1, SUN Yuyu4. Performance degradation trend prediction of rolling bearings based on SPA-FIG and optimized ELM. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(19): 187-194.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I19/187

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