TY - CHAP
T1 - Introduction
AU - Harrou, Fouzi
AU - Sun, Ying
AU - Hering, Amanda S.
AU - Madakyaru, Muddu
AU - Dairi, Abdelkader
N1 - KAUST Repository Item: Exported on 2021-03-02
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://hdl.handle.net/10754/667754
UR - https://linkinghub.elsevier.com/retrieve/pii/B9780128193655000073
U2 - 10.1016/b978-0-12-819365-5.00007-3
DO - 10.1016/b978-0-12-819365-5.00007-3
M3 - Chapter
SN - 9780128193655
SP - 1
EP - 17
BT - Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
PB - Elsevier
ER -