The client was the largest petrochemical company in South America, with multiple downstream plants and pipeline equipment. The company had a challenge in predicting maintenance requirements across their systems and subsystems. They required a scalable and high-quality deep learning solution for predictive maintenance to optimize the operations and minimize downtime.
The client needed to predict maintenance requirements across multiple systems and numerous subsystems in their pipeline equipment. They required a scalable solution that would be efficient and reliable in predicting the maintenance requirements. The company was facing challenges in optimizing operations and minimizing downtime without any prior knowledge of when the system might fail.
They also needed actionable insights to prioritize maintenance tasks based on criticality and failure probability.Limited visibility into real-time equipment health further complicated timely decision-making and resource allocation.
The Codvo.ai team approached the challenge by designing an efficient and scalable architecture that could accommodate an ever-increasing number of systems. They leveraged the C3.AI framework to create continuous integration and deployment of AI pipelines and a live generative display user interface.
The team followed a streamlined agile process for model development activity, including data pre-processing, feature engineering, model selection, and optimization. They used semi-automated adaptive model training and validation techniques, coupled with meticulous review and prudent evaluation, to ensure the accuracy and reliability of the predictions.
The team coordinated with Codvo Data Engineering and Application Development teams to ensure successful production deployment of the solution.
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The Codvo.ai team used Python Scikit Learn, Jupyter Notebook, Docker, GitHub, and the C3.AI platform for the development of the predictive maintenance solution.
-Achieved a 30% reduction in unplanned downtime, significantly improving operational continuity.
-Realized 25% cost savings in maintenance operations through early fault detection and prioritized task scheduling.
-Improved equipment health visibility with real-time data integration and intuitive dashboards.
-Enabled scalable predictive maintenance across hundreds of pipeline subsystems.
-Delivered highly accurate AI-driven maintenance predictions, minimizing guesswork and reactive repairs.
-Ensured seamless integration into existing workflows with support from Codvo’s cross-functional teams.
