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

• Main Authors: Nayher Clavijo, Afrânio Melo, Maurício M. Câmara, Thiago Feital, Thiago K. Anzai, Fabio C. Diehl, Pedro H. Thompson, José Carlos Pinto
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Processes
• Subjects: fault diagnosis; conditional-based maintenance; canonical variate analysis; fiscal meters; real oil and gas processing facility
• Online Access: https://www.mdpi.com/2227-9717/7/7/436

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.