Root Cause Analysis of Compressor Failure by Machine Learning
The machine learning revolution is starting to be implemented in machinery maintenance and has become inevitable in highly industrialized and integrated plants. These measures save time, money and effort through new and dynamic condition monitoring strategies. Moreover, knowledge can be extracted from these models about the potential root causes of machinery breakdowns. This results in key information to prevent similar situations in the future. Finding patterns in these breakdowns and root causes through advanced data analytics is not commonplace however. This case study then develops a strategy to implement predictive modeling and to perform root cause analysis on a compressor unit running at one of the largest refineries in Europe. Findings are presented to field experts and are deemed to add to their intuition due to the presence of unanticipated triggers. We show that root causes can be identified by constructing an intelligent data pipeline based on a multitude of readily available sensor data.
2019 Petroleum and Chemical Industry Conference Europe (PCIC EUROPE)