The base CatBoost model employed an autoencoder mod- el as a feature input, and to refine our approach, we lever- aged domain expertise from subject matter experts (SMEs) and conducted Physics-driven failure mode analysis. We further customized the model by incorporating informa- tion about each specific machine’s operating conditions and insights gathered from other machines to improve the model’s accuracy. Fractal deployed this solution on Azure, using Azure Data Lake for storage, Databricks for data analysis, and Azure Machine Learning for model training. Our offering: Engineered ESP success through predictive maintenance precision
TOOL
AIM
RESULT
To differentiate between normal and deviated equipment states
Improved accuracy in predicting the condition of equipment Enhanced capacity to predict equipment conditions An accurate representation of the intricate conditions that equipment may face Improved accuracy of the model in addressing ESP system conditions A significant boost in the precision and timeliness of equipment condition predictions
Deep Neural Network
An input to the primary CatBoost model
Autoencoder Model
To gain insights from SMEs and physics-driven failure mode analysis
Domain Expertise & Analysis
To learn from other similar machines to enhance model accuracy
Customized Machine Conditions
To increase precision and timeliness in predictions
Additional Model Enhancements
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