基于函数主元分析的多阶段退化过程建模与预测

退化过程建模与预测作为设备健康管理的基础,是降低运行风险和维护成本的有效途径。为解决实际中退化过程所表现出的随机性、非线性和多阶段复杂性,提出了一种基于函数主元分析的多阶段退化过程自适应建模与预测方法。通过将退化测量值视为连续函数的离散采样值,从而将退化建模问题转换为函数型数据分析问题。在此基础上,利用函数主元分析方法对退化数据进行降维,提取设备退化的共性信息以及个体差异性信息。结合贝叶斯推理,利用在线监测数据更新退化模型参数,实现健康状态的在线实时预测。最后,将所提的方法用于散热风扇的加速寿命试验数据,验证了本方法的有效性。结果表明,所提方法可以地很好建模多阶段的复杂随机退化过程,具有潜在的工程应用价值。

Multi phase degradation process modeling and prediction based on functional principal component analysis

As the foundation of equipment health management, the degradation process modeling and prediction is an effective way to reduce running risk and maintenance cost. In order to solve the randomness, nonlinearity and multi phase complexity of the degradation process in practice, an adaptive modeling and prediction method for multi phase degradation process based on functional principal component analysis is proposed. This method treats the degradation measurement values as discrete sample values of continuous function, and thus converts the degradation modeling problem into functional data analysis problem. On this basis, this method uses the function principal component analysis method to reduce the dimensionality of the degraded data, and extracts the common information and individual difference information of the equipment degradation. With Bayesian reasoning, the on line monitoring data is used to update the degradation model parameters to realize on line real time prediction of equipment health status. Finally, the proposed method is applied to the accelerated life test data of a cooling fan, and the effectiveness of the method is verified. The results show that the proposed method can well model the multi phase complex random degradation process and thus has potential engineering application value.