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Modelling Intern
il y a 3 semaines
Context
Metroscope software assists operators in maintaining the efficiency and availability of power generation assets, including combined cycle gas turbines (CCGT) and nuclear power plants (NPP).
Two technical solutions are developed by Metroscope today:
A "Diagnostics" module [1], based on a physics-based digital twin predicting the best performance, using plant sensor measurements to estimate deviation from the best performance and an algorithm inferring the most probable failure modes explaining the deviations.
An "Anomaly Detection" (AD) module, based on Advanced Pattern Recognition (APR) models, using machine learning algorithms trained on healthy plant data, that detect deviations and generate alerts.
The two solutions cover different but complementary scopes: the Diagnostics module monitors the full thermodynamic cycle of the power plant, detecting the root cause of the main sources of efficiency loss. The Anomaly Detection module offers more flexibility: any kind of sensor can be used to monitor any kind of system. However, it is not able to identify the root cause of the deviations.
Decision tree-based diagnosis is a long-known and proven method to determine the root cause of deviations ([2], [4]) on rotating machines as well as on energy generation assets. The inferential engine proposed by the Diagnostic module is only an evolution of the decision tree methodology with a more quantitative approach, but that was deployed primarily on a physical model ([7]).
The deviations found by APR models could directly be used to identify specific failure modes, based on industrial knowledge and Metroscope's diagnosis expertise.
The goal of the internship is to leverage pre-defined APR models and Metroscope's inferential engine to predict failure modes with a greater scope than the actual physical models. The systems monitored are the main systems of a CCGT power plant, as well as some critical auxiliaries. The domains covered go from thermodynamics to vibration analysis.
Missions
Once introduced to Metroscope solution and tools for modeling industrial installations, you will perform the following tasks:
Conduct a literature review on the different failure modes of a CCGT and the Root Cause Analysis (RCA) methods
Conduct a comparative study of the detections we have on both modules for similar systems
Build models on both modules on selected use cases
Combine the inferential engine with the AD models, using the deviations predicted to infer the root cause of the issue
Develop a workflow to automatically promote faults detections in the physical Diagnosis module based on the alerts of the AD module
