研究了同时含有隐含性能退化和输入故障的连续过程故障预测问题. 假设退化过程变化缓慢, 而输入故障突发且变化快速. 基于多尺度主元分析模型, 提出了一种针对性能退化过程的故障预测方法. 首先对一段正常工况下的历史数据进行离散小波分解, 对不同尺度下的小波系数建立相应的主成分分析模型. 经过多层小波分解, 性能退化过程会被低频系数模型检测到. 然后用基于重构的方法估计退化程度, 并用指数平滑方法对其进行预测, 最后预测出系统剩余有效寿命(RUL). 对 CSTR 的案例研究表明了该方法的有效性.

The fault prognosis problem for continuous processes with hidden performance degradation and input faults is studied. It is assumed the degradation process develops slowly and the input fault occurs suddenly and varies rapidly. Based on the multi-scale principal component analysis model, a fault prognosis method is proposed for the performance degradation process. We first apply discrete wavelet decomposition to a segment of historical data under normal operation condition, and then perform the principal component analysis on the wavelet coefficients for each scale. After multilayer wavelet decomposition, the degradation can be detected by the low frequency coefficients model. Then the degraded extent is estimated by a reconstruction-based method and predicted by an exponential smoothing approach. At last, the remaining useful life is predicted. A case study on continual stir tank reactor (CSTR) shows the efficiency of the proposed approach.