Data-Driven Prescriptive Maintenance: Failure Prediction Using Ensemble Support Vector Classification for Optimal Process and Maintenance Scheduling-Reference-Cited by-同舟云学术

Data-Driven Prescriptive Maintenance: Failure Prediction Using Ensemble Support Vector Classification for Optimal Process and Maintenance Scheduling

Published:2020-10-26 Issue:44 Volume:59 Page:19607-19622
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Gordon Christopher Ampofo Kwadwo¹²³,Burnak Baris¹²^ORCID,Onel Melis¹²,Pistikopoulos Efstratios N.¹²^ORCID

Affiliation:

1. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas 77843, United States

2. Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States

3. Mary Kay O’Connor Process Safety Center, College Station, Texas 77843, United States

Funder

Mary Kay O'Connor Process Safety Center

Texas A&M Energy Institute

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.0c03241

Reference54 articles.

1. The Evolution of Process Safety: Current Status and Future Direction

2. Interactions of Maintenance and Production Planning for Multipurpose Process PlantsA System Effectiveness Approach

3. Schleichert, O. P.; Bringmann, B.; Zablotskiy, S.; Kremer, H.; Köpfer, D. Predictive Maintenance; Deloitte Analytics Institute. Deloitte Consulting GmbH, 2017: Berlin, Germany.

4. Preventive maintenance policy optimization for multipurpose plant equipment

5. Flexibility, reliability and maintenance considerations in batch plant design under uncertainty

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