Introduction

Fouzi Harrou, Ying Sun, Amanda S. Hering, Muddu Madakyaru, Abdelkader Dairi

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

With today's competitive automation environment, demands for efficiency, safety, and high productivity are continuously increasing. Thus, process monitoring is vital for maintaining the desired process performance and specifications. Process monitoring aims to detect potential anomalies that can occur in a monitored process and identify their potential sources. This chapter provides an overview of process monitoring methods. To begin, we present the motivation for using process monitoring, followed by an introduction and a reminder of some of the key definitions, fundamental concepts, and terminology that are used throughout this chapter. We also briefly explain the distinction between different types of faults, such as drift, abrupt, and intermittent faults. In the following section, we discuss the different monitoring methods including model-, knowledge-, and data-based techniques. Finally, we describe the most commonly used metrics for the evaluation of the performance of the different fault detection approaches.
Original languageEnglish (US)
Title of host publicationStatistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
PublisherElsevier
Pages1-17
Number of pages17
ISBN (Print)9780128193655
DOIs
StatePublished - 2021

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