Predictive Maintenance Using Machine Learning Methods in Petrochemical Refineries

This research project evaluates the suitability of machine learning methods for early fault prediction and predictive maintenance in petrochemical refineries based on real- life use cases at Shell Pernis. Refineries are mature industrial installations, however, unplanned shutdowns still occur due to equipment failures. Refineries have petabytes of process control data available from the past years, however, all of that data is unla- belled. The goal of this research project was to evaluate, whether useful information can be extracted from the process control data. The resulting approach had to be compatible with Shell IT, scalable to larger sections of the refinery, reusable in other parts of the refinery and capable of detecting the components that cause the potential faults. During this research project, multiple solutions based on artificial neural net- works and statistical approaches were implemented to model the normal behaviour of the monitored systems. Abnormal predictions for the modelled systems were then used to predict failures in advance, where the prediction horizon reached more than a month for some use cases. 4-layer GRUs with tanh activation functions and an input sequence length of 4 samples provided the best results. GRUs were 7% faster to train than LSTMs while reducing the prediction error by 15%. Furthermore, the predic- tion error was less than 3% for the normal operating conditions while reaching more than 15% prior to failures. Therefore, machine learning models can predict failures in petrochemical refineries without any industry-specific knowledge, if the model is trained with clean data that does not contain abnormal time series