Development and Application of a Data-Driven System for Sensor Fault Diagnosis in an Oil Processing Plant

Nayher Clavijo; Afrânio Melo; Maurício M. Câmara; Thiago Feital; Thiago K. Anzai; Fabio C. Diehl; Pedro H. Thompson; José Carlos Pinto

LAPSE

Living Archive for Process Systems Engineering

LAPSE:2019.1015

Published Article

LAPSE:2019.1015

Development and Application of a Data-Driven System for Sensor Fault Diagnosis in an Oil Processing Plant

Nayher Clavijo, Afrânio Melo, Maurício M. Câmara, Thiago Feital, Thiago K. Anzai, Fabio C. Diehl, Pedro H. Thompson, José Carlos Pinto

September 23, 2019

Predictive analytics is usually cited as one of the most important pillars of the digital transformation. For the oil industry, specifically, it is a common belief that issues like integrity and maintenance could benefit from predictive analytics. This paper presents the development and the application of a process-monitoring tool in a real process facility. The PMA (Predictive Maintenance Application) system is a data-driven application that uses a multivariate analysis in order to predict the system behavior. Results show that the use of a multivariate approach for process monitoring could not only detect an early failure at a metering system days before the operation crew, but could also successfully identify, among hundreds of variables, the root cause of the abnormal situation. By applying such an approach, a better performance of the monitored equipment is expected, decreasing its downtime.

Record ID

LAPSE:2019.1015

Keywords

canonical variate analysis, conditional-based maintenance, fault diagnosis, fiscal meters, real oil and gas processing facility

Subject

Intelligent Systems

Suggested Citation

Clavijo N, Melo A, Câmara MM, Feital T, Anzai TK, Diehl FC, Thompson PH, Pinto JC. Development and Application of a Data-Driven System for Sensor Fault Diagnosis in an Oil Processing Plant. (2019). LAPSE:2019.1015

Author Affiliations

Clavijo N: Programa de Engenharia Química/COPPE, Universidade Federal do Rio de Janeiro, Cidade Universitária, CP 68502, CEP 21941-972 Rio de Janeiro, Brazil [ORCID]
Melo A: Programa de Engenharia Química/COPPE, Universidade Federal do Rio de Janeiro, Cidade Universitária, CP 68502, CEP 21941-972 Rio de Janeiro, Brazil
Câmara MM: Programa de Engenharia Química/COPPE, Universidade Federal do Rio de Janeiro, Cidade Universitária, CP 68502, CEP 21941-972 Rio de Janeiro, Brazil [ORCID]
Feital T: Programa de Engenharia Química/COPPE, Universidade Federal do Rio de Janeiro, Cidade Universitária, CP 68502, CEP 21941-972 Rio de Janeiro, Brazil; OptimaTech Ltd., CEP 21941-614 Rio de Janeiro, Brazil
Anzai TK: Centro de Pesquisas Leopoldo Americo Miguez de Mello—CENPES, Petrobras—Petróleo Brasileiro SA, CEP 21941-915, Rio de Janeiro, Brazil
Diehl FC: Centro de Pesquisas Leopoldo Americo Miguez de Mello—CENPES, Petrobras—Petróleo Brasileiro SA, CEP 21941-915, Rio de Janeiro, Brazil
Thompson PH: Centro de Pesquisas Leopoldo Americo Miguez de Mello—CENPES, Petrobras—Petróleo Brasileiro SA, CEP 21941-915, Rio de Janeiro, Brazil
Pinto JC: Programa de Engenharia Química/COPPE, Universidade Federal do Rio de Janeiro, Cidade Universitária, CP 68502, CEP 21941-972 Rio de Janeiro, Brazil
[Login] to see author email addresses.

Journal Name

Processes

Volume

7

Issue

7

Article Number

E436

Year

2019

Publication Date

2019-07-10