Despite tremendous efforts, AI-based predictive maintenance is still not fully exploited in Oil&Gas plants worldwide. The reason mainly relies on the fact that predictive maintenance algorithms need many examples of failures to be trained on, and this is not always the case. For this reason, we developed an efficient unsupervised approach for predictive maintenance, based on deep learning algorithms and applied successfully to predict and anticipate the failures of a coalescer of an Eni's offshore plant.
Our method is based on a Recurrent Neural Network (RNN) autoencoder architecture, coupled with clustering algorithms. The RNN is based on a combination of two algorithmic steps, respectively called encoder and decoder. The encoder reads multivariate chunks of data and summarizes them in a vector of fixed length, named context vector. Then, the decoder brings this context vector and reconstructs the input signals. Once the reconstruction error is minimized, we cluster context vector by choosing an optimal number of clusters and associating them to the operating conditions of the equipment, in particular by distinguishing ‘healthy’ from ‘faulty’ states.
We applied the aforementioned workflow to distinguish the operating conditions of a small equipment in an Eni's offshore plant. This equipment, an electroastic coalescer, suffered repeated troubles during the first phases of plant start-up. We picked up all the sensor measurements available for the coalescer (pressures, levels, temperatures) with very tight sampling (10 seconds resolution) and trained the RNN architecture on 9 months of data. After the application of a suitable clustering method on the context vector minimizing reconstruction error, we were then able to detect up to 5 different operating conditions of the coalescer, associating them to healthy and faulty states of it. In particular, the method was able to authomatically cluster the failures periods of the coalescer, with an advance of around 4 hours before the failures occurred.
‘Effective unsupervised learning – learning without labelled data – remains a holy grail of AI’ (Andrew Ng, WIPO Technology Trends 2019, Artificial Intelligence). We tried to do a step forward in the application of unsupervised approaches to predictive maintenance of industrial equipments by developing an innovative deep learning based method and applying it to a coalescer of an Oil&Gas plant, getting results that are very promising for massive, large scale application in real production settings.