An Enhanced Unsupervised Extreme Learning Machine Based Method for the Nonlinear Fault Detection

Although the unsupervised extreme learning machine (UELM) based methods have been widely used to diagnosis the nonlinear process faults recently, the UELM algorithm is only designed to preserve the local adjacency similarity of the input dataset instead of mining the intra-class variations. Besides, the determination of the optimal UELM hidden nodes number is a tough issue. In order to deal with these two problems, a novel enhanced UELM (EUELM) based scheme is developed to effectively detect the nonlinear process faults in our work. In the proposed EUELM approach, the UELM algorithm is first improved by naturally incorporating the diversity analysis technique into the original UELM objective function to preserve both the intra-class variation and the local adjacency similarity of the input dataset. Then, to settle the difficult issue of selecting the optimal number of hidden nodes, kernel trick is further employed in the EUELM approach to mine the data strong nonlinearity. Based on the extracted diversity and local similarity low dimensional feature information, the k-nearest neighbor (KNN) principle is applied to derive a monitoring statistic for fault detection. At last, the experiments and comparisons on the monitoring effectiveness of the suggested EUELM based approach are made on a numerical nonlinear system and the benchmark Tennessee Eastman (TE) process. The obtained monitoring results illustrate that the significant improvements can be achieved by the proposed EUELM based fault detection approach compared with other popular and related approaches.