中文摘要：

针对复杂时变工业过程实时故障诊断问题，提出了一种基于提升小波（1ifting wavelet，LW）与递归增量聚类（recursive incremental clustering，RICLUSTER）相结合的实时故障诊断方法（1ifting wavelet—recursive incremental clustering，LW—RICLUSTER）。该方法首先通过LW变换对数据实时去噪，再通过RICLUSTER实时监控。由于采用LW与RICLUSTER相结合的方法，节省存储空间和运算时间的同时提高了诊断精度。实验结果表明，LW—RICLUSTER集合方法能有效实现时变过程监控，在诊断精度、速度和适应性方面，优于传统单一型CLUSTER方法。

英文摘要：

An ensemble real-time fault diagnosis method based on lifting wavelet （LW） and recursive incre- mental clustering（RICLUSTER）, called LW RICLUSTER, is proposed to realize real-time monitoring for com- plex time-varying industrial processes. Firstly, data are denoised by LW transform in real-time, then RICLUS- TER is used for real-time monitoring. With the ensemble approach, storage space is saved and computing time is shortened, while the precision of diagnostic is increased. Experiment results show that the LW-RICLUSTER algorithm can monitor time-varying process. The LW-RICLUSTER is superior to the traditional single CLUS- TER in diagnosis precision, rate and adaptability