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

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.

Improved Fault Detection in Chemical Engineering Processes via Non-Parametric Kolmogorov–Smirnov-Based Monitoring Strategy

“…Efficient and continuous monitoring of key process variables is crucial for optimizing complex chemical and petrochemical processes [1,2]. The primary objective is not only to enhance productivity but also to prevent catastrophic incidents in the event of a failure [3,4]. Several severe accidents have underscored the importance of timely fault detection in chemical and petrochemical plants globally over the past few decades.…”

Discriminative Extreme Learning Machine with Cross‐Domain Mean Approximation for Unsupervised Domain Adaptation

“…Semi-supervised ELM usually requires unlabeled data together with labeled data to train models well, including Laplacian Twin Extreme Learning Machine (Lap-TELM) [23], Semi-Supervised Extreme Learning Machine (SS-ELM) [24], Robust Semi-Supervised Extreme Learning Machine (RSS-ELM) [25], and Adaptive Safe Semi-Supervised Extreme Learning Machine (AdSafe-SSELM) [26]. In some other cases where no labeled data are available, some Unsupervised ELM (USELM) algorithms are proposed for clustering, dimension reduction, or data representation, such as Unsupervised Extreme Learning Machine (USELM) [24], Extreme Learning Machine as an Auto-Encoder (ELM-AE) [27], Enhanced Unsupervised Extreme Learning Machine (EUELM) [28], and Unsupervised Feature Selection based Extreme Learning Machine (UFSELM) [29]. Moreover, as deep learning has been successful in many fields, deep ELMs are also developed to extract more abstract and expressive features, such as Kernel-based Multi-Layer Extreme Learning Machine ELM (ML-KELM) [30], Hierarchical-ELM (H-ELM) [31], DS-ELM (a deep and stable extreme learning machine) [32], and Deep Residual Compensation Extreme Learning Machine (DRC-ELM) [33].…”

Cholesky Factorization Based Online Sequential Multiple Kernel Extreme Learning Machine Algorithm for a Cement Clinker Free Lime Content Prediction Model

“…In recent years, the extreme learning machine (ELM) [22,23] and kernel extreme learning machine (KELM) [24] are proposed and have been successfully applied to model identification, such as saliency detection [25], gesture recognition [26], image classification [27], nonlinear fault detection [28], seepage time soft sensor model of nonwoven fabric [29], and rolling bearing sub-health recognition [30]. However, different kernel functions have different characteristics, and the performances are varying in different applications.…”