Talk: A Novel Framework for Predictive Maintenance Using Deep Learning and Reliability Engineering
10:30 - 11am
The oil and gas plant’s equipment usually has a long-life cycle. During its O&M (Operation and Maintenance) phase, since the accidental occurrence of offshore plant equipment causes catastrophic damage, it is necessary to make more efforts for managing critical offshore equipment. Prognostics estimation from multi-sensor time series data is useful to enable condition- based maintenance and ensure high operational availability of equipment. We propose a novel deep learning based approach for Prognostics that is useful in scenarios where: (i) access to labelled failure data is scarce due to rarity of failures (ii) future operational conditions are unobserved and (iii) inherent noise is present in the sensor readings. All three scenarios mentioned are unavoidable sources of uncertainty and often resulting in unreliable predictions to be considered for maintenance interventions and planning. The solution utilizes real time data from an operational oil and gas production facility in the UK North and combines engineering failure mechanics, reliability engineering and machine learning. A journey that involved building and optimise 200+ recurrent neural networks using SigOpt, a probabilistic optimization engine with an end-to-end cloud solution resulting in increased asset uptime, lower operating costs, and environmental impacts.