Chris Aldrich

By Chris Aldrich

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 extended in multiple ways to time series analysis.

Part of the book: Time Series Analysis

By Chris Aldrich

A novel approach to anomaly detection in time series data is based on the use of multivariate image analysis techniques. With this approach, time series are encoded as images that make them amenable to analysis by pretrained deep neural networks. Few studies have evaluated the merits of the different image encoding algorithms, and in this investigation, encoding of time series data with Euclidean distance plots or unthresholded recurrence plots, Gramian angular fields, Morlet wavelet scalograms, and an ad hoc approach based on the presentation of the raw time series data in a stacked format are compared. This is done based on three case studies where features are extracted from the images with gray level co-occurrence matrices, local binary patterns and the use of a pretrained convolutional neural network, GoogleNet. Although no method consistently outperformed all the other methods, the Euclidean distance plots and GoogleNet features yielded the best results.

Part of the book: Time Series Analysis - Recent Advances, New Perspectives and Applications [Working title]