College of Information and Control Engineering, China University of Petroleum, Qingdao 266580 China.
College of Information and Control Engineering, China University of Petroleum, Qingdao 266580 China.
ISA Trans. 2018 Jan;72:218-228. doi: 10.1016/j.isatra.2017.09.015. Epub 2017 Oct 7.
Traditional kernel principal component analysis (KPCA) based nonlinear process monitoring method may not perform well because its Gaussian distribution assumption is often violated in the real industrial processes. To overcome this deficiency, this paper proposes a modified KPCA method based on double-weighted local outlier factor (DWLOF-KPCA). In order to avoid the assumption of specific data distribution, local outlier factor (LOF) is introduced to construct two LOF-based monitoring statistics, which are used to substitute for the traditional T and SPE statistics, respectively. To provide better online monitoring performance, a double-weighted LOF method is further designed, which assigns the weights for each component to highlight the key components with significant fault information, and uses the moving window to weight the historical statistics for reducing the drastic fluctuations in the monitoring results. Finally, simulations on a numerical example and the Tennessee Eastman (TE) benchmark process are used to demonstrate the superiority of the proposed DWLOF-KPCA method.
传统基于核主元分析（KPCA）的非线性过程监测方法可能表现不佳，因为其在实际工业过程中经常违反高斯分布假设。为了克服这一不足，本文提出了一种基于双加权局部离群因子（DWLOF-KPCA）的改进 KPCA 方法。为了避免特定数据分布的假设，引入局部离群因子（LOF）来构建两个基于 LOF 的监测统计量，分别用于替代传统的 T 和 SPE 统计量。为了提供更好的在线监测性能，进一步设计了一种双加权 LOF 方法，为每个分量分配权重，以突出具有显著故障信息的关键分量，并使用移动窗口对历史统计量进行加权，以减少监测结果的剧烈波动。最后，通过数值示例和田纳西伊斯曼（TE）基准过程的仿真，验证了所提出的 DWLOF-KPCA 方法的优越性。
