Online process monitoring and fault-detection approach based on adaptive neighborhood preserving embedding

Lei Tan,Yong Chen,Aimin Miao,Peng Li

doi:10.1177/0020294019838580

Abstract

This study aims to solve the problem involving the high false alarm rate experienced during the detection process when using the traditional multivariate statistical process monitoring method. In addition, the existing model cannot be updated according to the actual situation. This article proposes a novel adaptive neighborhood preserving embedding algorithm as well as an online fault-detection approach based on adaptive neighborhood preserving embedding. This approach combines the approximate linear dependence condition with neighborhood preserving embedding. According to the newly proposed update strategy, the algorithm can achieve an adaptive update model that realizes the online fault detection of processes. The effectiveness and feasibility of the proposed approach are verified by experiments of the Tennessee Eastman process. Theoretical analysis and application experiment of Tennessee Eastman process demonstrate that in this article proposed fault-detection method based on adaptive neighborhood preserving embedding can effectively reduce the false alarm rate and improve the fault-detection performance.

Highlights

With the increasing scale and complexity industrial process, the fault detection of the entire process has become the focus of research in the field of process control
According to the realtime data obtained to update the model online, the adaptive neighborhood preserving embedding (ANPE) method will greatly improve the monitoring performance of the industrial process
In order to reduce the false alarm rates (FARs) of fault detection for industrial processes, this article presents a new method based on the ANPE algorithm for fault detection, which is different from the traditional neighborhood preserving embedding (NPE) method

Summary

Introduction

With the increasing scale and complexity industrial process, the fault detection of the entire process has become the focus of research in the field of process control. There is a need for multivariate statistical process monitoring (MSPM).[8,9]. MSPM is widely used in chemical, power, machinery, and others industrial processes based on the interrelationships between multiple sets of measurement data.[10,11,12,13] Process monitoring and fault-detection methods that are based on multivariate statistical projection theory are widely used.[14] In MSPM, key information pertaining to the data is mapped into the lowdimensional space by the data dimension reduction, and the original high-dimensional data feature information is obtained, after which comprehensive statistics are established for the low-dimensional data to realize online monitoring. MSPM methods include principal component analysis (PCA),[15,16] canonical correlation analysis (CCA),[17,18] independent component analysis (ICA),[19,20] Fisher discriminant analysis (FDA),[21,22] and partial least squares (PLS).[23,24]

Objectives

Methods

Findings

Conclusion