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Client Overview

Our client operates within the HVAC (Heating Ventilation and Air Conditioning) Service Provider, where they specialize in high-quality heating, ventilation, and air conditioning systems. They have a critical business imperative to optimize compressor performance and reliability by harnessing AI/ML techniques, with a specific focus on the detection of short cycles in their compressor units.

Challenges

  • The client encounters a challenge stemming from disruptions or anomalies within the dataset, impacting their ability to work with reliable data.
  • The client encounters a substantial challenge stemming from improperly labeled data, which hinders their ability to address the issue of detecting short cycles in compressors effectively.
  • Identifying and detecting short cycles in the compressor poses a significant challenge for the client.

Our Solution

We provide a comprehensive solution to our client that helps to solve all the complex challenges arising that are:

  • We employed data extraction and data cleansing techniques to assist the client in overcoming their challenges related to data quality, ensuring more accurate short-cycle detection.
  • Leveraging the XGBoost Library, we developed and trained a robust machine-learning model to process the enhanced data, aiding in the identification of short cycles in compressors.
  • We facilitated the deployment of the solution by integrating it with GitHub and utilizing AWS CodePipeline, streamlining the process and ensuring efficient code deployment to AWS auto-scaling.
  • To optimize resource utilization, we implemented a scheduler that dynamically scales up or down the number of running services in Amazon Elastic Container Service (ECS) based on predefined time triggers.
  • We created a service within AWS ECS to run multiple services as containers within a Fargate cluster, enhancing the efficiency and manageability of the deployment.
  • The AWS Application Autoscaling service was configured to operate for a predefined duration, automatically adjusting the number of running services based on demand. Once the designated time elapsed, the service gracefully stopped running to optimize resource usage.

Data Flow Diagram

data-ingression-engine

Conclusion

By leveraging AI and ML technologies for Compressor Diagnosis, we developed a model with an F1 score of 0.86 and implemented it in production. This transition marked a significant shift from a "fail and fix" approach to a more intelligent solution, allowing our client to proactively monitor and improve model performance, ultimately leading to more informed and effective decision-making.

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