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Published in:
2013 | OriginalPaper | Chapter
1. Introduction
Authors : Chris Aldrich, Lidia Auret
Published in: Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods
Publisher: Springer London
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Abstract
In this introductory chapter, the drivers of process monitoring technology are reviewed to ensure safe, profitable and environmentally responsible process operation. The resultant trends are considered in terms of developments in instrumentation, computational and telecommunications hardware and process analytical developments and data-driven control strategies. Moreover, a generalized framework for data-driven fault diagnosis is discussed, as well as the role of machine learning in this framework. This framework consists of a data matrix representative of the process, a diagnostic feature matrix, a reconstructed data matrix and a residual matrix. The feature, reconstructed data and residual matrices are all derived from the data matrix. This can be accomplished by different methods, and some of those based on machine learning are summarized in broad terms, focusing on supervised and unsupervised learning, semi-supervised learning, reinforcement learning and self-taught or transfer learning.