Case studies

Efficiency & Cost Savings Codvo's Petrochem Predictive Maintenance Success

About the client

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.

Overview team worked on an end-to-end deployment of the C3.AI platform industrial asset management and maintenance solution. The team provided a solution for predictive maintenance in downstream plants for the client. They used the C3.AI framework and leveraged it to create continuous integration and deployment of AI pipelines. The team also designed an efficient and scalable architecture to accommodate an ever-increasing number of systems.

Business Challenge

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.

Our Approach and Solution

The 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.

Tech Stack

The team used Python Scikit Learn, Jupyter Notebook, Docker, GitHub, and the C3.AI platform for the development of the predictive maintenance solution.


Business Impact

The team successfully completed the deployment of the predictive maintenance solution in the downstream plants for the client.

The team accomplished the delivery on time with high-quality development and result-oriented approaches.

The team conducted successful data engineering, 60+ ML model training, and validation for the AI predictions in Pilot Deployment.
The solution provided by the Codvo.AI team enabled the client to predict maintenance requirements and optimize operations, leading to minimized downtime and increased productivity.
The solution had a significant impact on the client's business by reducing costs and improving efficiency.