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SigOpt Summit 2021
November 16, 2021, Online
SigOpt Summit 2021
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SigOpt Summit 2021
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A Novel Framework for Predictive Maintenance Using Deep Learning and Reliability Engineering
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​​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.

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Alex Lowden
Data Science Manager & Team Lead at Accenture
Shayan Mortazavi
Data Science Manager at Accenture

Alex Lowden is a Data Science Manager & Team Lead within the AI practice of Accenture where he is predominantly focused on applying machine learning to unlock value within the Energy & Chemicals industries. Alex has a unique blend of analytical and operational experience, working as a Petroleum Engineer in the UK North Sea on fast-paced operational projects, as well as multiple years’ experience building analytical solutions across a variety of industries ranging from Aerospace to Agriculture. He also holds a BSc in Mathematics, and an MSc in Petroleum Engineering from Imperial College London where he received the Colin Wall Award for his work on modelling transient temperature profiles in Ultra-Deep-Water wells.

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Shayan Mortazavi is a Data Science Manager at Accenture Industrial Analytics Group, where he is currently working within resources industries with interests in predictive maintenance, digital plant engineering, and optimization for upstream industries and the water industry. Shayan has over 15 years of experience in material science, engineering consulting, data science, and digital change. In his recent work, Shayan is developing scalable machine learning based solutions for the industry by applying Bayesian inference, dynamic control and graph network decision making systems. Shayan received his MSc from the University of Newcastle in Marine Science and his BSc in Fluid Dynamic and Structural Design. He is a chartered member of IMECHE and a winner of TD Williamson award for building a novel machine learning solution to detect and locate leaks in transmission pipelines.

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Alex Lowden
Shayan Mortazavi