考虑退化模式动态转移的健康状态自适应预测

Self-adaptive Health Condition Prediction Considering Dynamic Transfer of Degradation Mode

为了更加精确地在设备退化过程中对其健康状态进行预测,本文深入研究了设备处于不同健康状态时的数据特点,针对现有单一预测方法的特点与不足,引入了退化模式的划分方法,并对不同的预测模型与退化模式的关系进行分析.进而建立"模式–模型"关联表,并通过关联表优选预测模型,实现了考虑退化模式动态转移的健康状态自适应预测以及剩余寿命估计.最后,以滚动轴承实验为实例,对该轴承进行了健康状态预测与剩余寿命估计.实验结果表明本方法较精确地预测了轴承的剩余寿命,证明了方法的有效性.

This paper investigates the data characteristics of engineering equipment under different health conditions in order to realize more accurate health state prediction during its degradation process. An approach to classify the degradation modes is introduced to overcome the shortcomings of the single-prediction-method existing in current applications. Based on an analysis on the relationship between different prediction models and degradation modes, an association table ＂Mode-Model＂ is established, thereby the self-adaptive health condition prediction and residual life estimation can be achieved by optimal selection of prediction models, which considers the dynamic transfer of degradation mode. Finally, the efficiency of the proposed method is verified by a rolling bearing experiment.