Project Name
How Ksolves Solved the Detection of Short Cycling in Compressors (Faulty Refrigerators) Using Machine Learning?
Our client was a leading provider of solutions for refrigeration systems. They faced challenges, including short compressor cycles in refrigerators, which increased power consumption and could damage the compressor. The client was looking for a solution that would help them detect and prevent these short cycles.
(A short cycle usually happens when the cooling becomes shorter than the usual time, and because of this, the compressor will turned on and off more as per the situation)
Our client were facing multiple challenges that include:
- Handling large volumes of unstructured data requires a robust data extraction and cleaning strategy.
- Facing issues in creating rules for average and short cycles domain due to lack of expertise and understanding of compressor behavior.
- Issues with integration and dependency while deploying CodeBuild.
- The major issues are setting up ECS, fargate clusters, and task definitions with network and security.
We have provided them with a robust solution that includes:
- We helped them to do data extraction that extracted AMPS values from unstructured data and organized them based on date.
- Our team imported the model XGBRegressor() class from the XGBoost library, and the hyper-parameters were passed as arguments.
- They cleaned data that removed noisy data through filters, ensured data quality, and handled missing values by preparing the dataset.
- We set up two different pipelines using AWS CodePipeline and CodeBuild. Our team also created Docker images and stored them in AWS ECR for model training and prediction.
- Our team successfully implemented a workflow to calculate the number of short cycles using an algorithm based on segment counts.
Finally, our team effectively addressed all the client challenges by utilizing advanced machine learning techniques that ensured the accurate detection of compressor short cycling. Moreover, the integration of our ML model for the client’s infrastructure is coupled with automated pipelines. Hence, our client has a robust and scalable solution for ongoing monitoring and preventing compressor short cycles in refrigeration systems.
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