Conclusion and further research directions

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

doi:10.1016/b978-0-12-819365-5.00015-2

Conclusion and further research directions

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

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

Developing efficient anomaly detection and isolation schemes that offer early detection of potential anomalies in the monitored process and identify and isolate the source of the detected anomalies is indispensable to monitor process operations in an efficient manner. This will further enhance availability, operation reliability, and profitability of monitored processes and reduce manpower costs. This book is mainly devoted to data-driven fault detection and isolation methods based on multivariate statistical monitoring techniques and deep learning methods. In this chapter, conclusions and further research directions are drawn.

Original language	English (US)
Title of host publication	Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches
Publisher	Elsevier
Pages	305-309
Number of pages	5
ISBN (Print)	9780128193655
DOIs	https://doi.org/10.1016/b978-0-12-819365-5.00015-2
State	Published - 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-03-02

Access to Document

10.1016/b978-0-12-819365-5.00015-2

Cite this

@inbook{20ddae01814a4befbda6406b3b365e79,

title = "Conclusion and further research directions",

abstract = "Developing efficient anomaly detection and isolation schemes that offer early detection of potential anomalies in the monitored process and identify and isolate the source of the detected anomalies is indispensable to monitor process operations in an efficient manner. This will further enhance availability, operation reliability, and profitability of monitored processes and reduce manpower costs. This book is mainly devoted to data-driven fault detection and isolation methods based on multivariate statistical monitoring techniques and deep learning methods. In this chapter, conclusions and further research directions are drawn.",

author = "Fouzi Harrou and Ying Sun and Hering, {Amanda S.} and Muddu Madakyaru and Abdelkader Dairi",

note = "KAUST Repository Item: Exported on 2021-03-02",

year = "2021",

doi = "10.1016/b978-0-12-819365-5.00015-2",

language = "English (US)",

isbn = "9780128193655",

pages = "305--309",

booktitle = "Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches",

publisher = "Elsevier",

address = "Netherlands",

}

TY - CHAP

T1 - Conclusion and further research directions

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 - Developing efficient anomaly detection and isolation schemes that offer early detection of potential anomalies in the monitored process and identify and isolate the source of the detected anomalies is indispensable to monitor process operations in an efficient manner. This will further enhance availability, operation reliability, and profitability of monitored processes and reduce manpower costs. This book is mainly devoted to data-driven fault detection and isolation methods based on multivariate statistical monitoring techniques and deep learning methods. In this chapter, conclusions and further research directions are drawn.

AB - Developing efficient anomaly detection and isolation schemes that offer early detection of potential anomalies in the monitored process and identify and isolate the source of the detected anomalies is indispensable to monitor process operations in an efficient manner. This will further enhance availability, operation reliability, and profitability of monitored processes and reduce manpower costs. This book is mainly devoted to data-driven fault detection and isolation methods based on multivariate statistical monitoring techniques and deep learning methods. In this chapter, conclusions and further research directions are drawn.

UR - http://hdl.handle.net/10754/667738

UR - https://linkinghub.elsevier.com/retrieve/pii/B9780128193655000152

U2 - 10.1016/b978-0-12-819365-5.00015-2

DO - 10.1016/b978-0-12-819365-5.00015-2

M3 - Chapter

SN - 9780128193655

SP - 305

EP - 309

BT - Statistical Process Monitoring Using Advanced Data-Driven and Deep Learning Approaches

PB - Elsevier

ER -

Conclusion and further research directions

Abstract

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this