基于通勤时间距离的LE污水处理过程故障检测方法

污水处理过程的性能监测与故障诊断，对于保障污水处理过程正常运行及保证出水质量达标具有重要意义.针对污水处理过程数据具有非线性、不确定性及且易受随机噪声影响等特征，提出了一种新的基于通勤时间距离的LE流形学习算法实现对复杂过程数据的特征提取.改进算法采用通勤时间距离方式进行样本间的相似度衡量并构造邻域图，理论分析和仿真测试表明改进算法可有效克服基本LE算法的邻域参数敏感问题并提高了算法的鲁棒性.将基于通勤时间距离的LE流形学习算法用于污水处理过程故障检测建模，在低维流形子空间构造综合统计量进行过程监测.应用结果表明，与基于PCA方法和LE方法的故障检测模型相比，基于改进算法的故障检测模型可及时探测故障的发生，具有较低的故障漏报率和故障误报率.为污水处理等复杂工业过程的故障监测提供了一种可行的解决方案.

Fault detection of wastewater treatment processes by using commute time distance based LE algorithm

Performance monitoring and fault diagnosis for wastewater treatment processes was of great significance for safeguarding the normal operation of the treatment process and ensuring the standard quality of effluent water. Aiming at the problems of nonlinearity, uncertainty and susceptibility to random noises in wastewater treatment process, an improved Laplacian Eigenmap (LE) manifold learning algorithm based on commuting time distance (CTD) was proposed to realize the feature extraction of the complex process data. In this algorithm, CTD was used to measure the similarity between samples and construct the neighborhood graph. Both theoretical analysis and simulation test proved that the proposed algorithm could efficiently overcome the sensitivity problem caused by neighborhood parameter and improve the robustness of the normal LE algorithm. Then the CTD based LE algorithm was applied in fault detection modeling for actual wastewater treatment process, and the fault monitoring statistic was constructed in the low-dimensional feature subspace. Application results showed that CTD-LE based model can timely detect the faults with lower missing rate and false rate as compared with normal PCA based model and normal LE based model. Application results showed that this method could provide a feasible solution for fault monitoring of complex industrial processes such as wastewater treatment.