A smart manufacturing methodology for real time chemical process diagnosis using causal link assessment-Reference-Cited by-同舟云学术

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A smart manufacturing methodology for real time chemical process diagnosis using causal link assessment

Published:2016-07-22 Issue:9 Volume:62 Page:3420-3431
ISSN:0001-1541
Container-title:AIChE Journal
language:en
Short-container-title:AIChE J.

Author:

Rathinasabapathy Rajan¹,Elsass Michael J.²,Josephson John R.³,Davis James F.⁴

Affiliation:

1. Dept. Chemical and Biomolecular Engineering, UCLA; Los Angeles CA 90095 (currently with Technical Services, Phillips 66)

2. Dept. of Chemical and Materials Engineering; University of Dayton; Dayton OH 45469

3. Dept. of Computer and Information Science; The Ohio State University; Columbus OH 43210

4. Dept. of Chemical and Biomolecular Engineering; UCLA; Los Angeles CA 90095

Publisher

Wiley

Subject

General Chemical Engineering,Environmental Engineering,Biotechnology

Reference40 articles.

1. A review of process fault detection and diagnosis. Part I: quantitative model-based methods;Venkatasubramanian;Comput Chem Eng.,2003

2. A review of process fault detection and diagnosis. Part II: qualitative models and search strategies;Venkatasubramanian;Comput Chem Eng.,2003

3. A review of process fault detection and diagnosis. Part III: process history based methods;Venkatasubramanian;Comput Chem Eng.,2003

4. Process data analysis and interpretation;Davis;Adv Chem Eng.,1999

5. Fault Detection and Diagnosis in Industrial Systems

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2. A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing;Processes;2023-01-24

3. Observational process data analytics using causal inference;AIChE Journal;2022-12-20

4. Challenges and opportunities of deep learning-based process fault detection and diagnosis: a review;Neural Computing and Applications;2022-11-18

5. Development of Innovative Operational Flexibility Measurement Model for Smart Systems in Industry 4.0 Paradigm;IEEE Access;2022

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