Mining Complex Patterns in Time Series Data

Chapter 1

Chris Aldrich

Abstract

Process Fault Diagnosis for

Continuous Dynamic Systems

Over Multivariate Time Series

Fault diagnosis in continuous dynamic systems can be challenging, since the variables in these systems are typically characterized by autocorrelation, as well as time variant parameters, such as mean vectors, covariance matrices, and higher order statistics, which are not handled well by methods designed for steady state systems. In dynamic systems, steady state approaches are extended to deal with these problems, essentially through feature extraction designed to capture the process dynamics from the time series. In this chapter, recent advances in feature extraction from signals or multivariate time series are reviewed. These methods can subsequently be considered in a classical statistical monitoring framework, such as

used for steady state systems. In addition, an extension of nonlinear signal processing based on the use of unthresholded or global recurrence quantification analysis is discussed, where two multivariate image methods based on gray level co-occurrence matrices and local binary patterns are used to extract features from time series. When considering the well-known simulated Tennessee Eastman process in chemical engineering, it is shown that time series features obtained with this approach can be an effective means of discriminating between different fault conditions in the system. The approach provides a general framework that can be

Keywords: process fault diagnosis, statistical process control, machine learning,

In the process industries, advanced process control is widely recognized as essential to meet the challenges arising from the trend toward more complex, larger scale circuit configurations, plant-wide integration, and having to make do with fewer personnel. In these environments, characterized by highly automated process operations, algorithms to detect and classify abnormal trends in process measure-

Process diagnostic algorithms can be derived from a continuum spanning first principle models on one end to entirely data driven or statistical models on the other. The latter is typically based on historical process data and is seen as the most cost effective approach to deal with complex systems. As a consequence, diagnostic

methods have seen considerable growth over the last couple of decades.

extended in multiple ways to time series analysis.

time series analysis, deep learning

ments are critically important.

1. Introduction

3
