Abstract
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.
Highlights
With the increasing demand of industrial process security and reliability, fault detection technology has been paid more and more attention
Standard unsupervised extreme learning machine (UELM) model has no explicit constraint condition for distant samples in original input space. This would lead to take no account of significant variance information of original process data in the UELM model, and the faraway input samples are inclined to be projected to a small adjacent area in the output space
The nearest neighbor number k of the k-nearest neighbor (KNN) rule used in the enhanced UELM (EUELM) is empirically chosen to be 4, and the tradeoff parameter λ is determined as 0.1 using the grid search
Summary
With the increasing demand of industrial process security and reliability, fault detection technology has been paid more and more attention. To guarantee the efficient fault detection performance, the optimal number of UELM hidden nodes needs to be chosen, which is a troublesome and intractable task using the existing parameter selection approaches. On the basis of the aforementioned analysis, a new monitoring approach using an enhanced UELM (EUELM) model is developed to detect the nonlinear process fault in this paper. The goal of the EUELM model is to preserve both the intra-class diversity information and local adjacency similarity structure of the original input data. (1) To extract the intra-class variations and local adjacency similarity structure of the input data, the intra-class diversity analysis is infused with the traditional UELM model, which is beneficial to improve the process monitoring effect.
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