Artificial Intelligence in Manufacturing Sector - Case Study

AI Application in Healthcare Industry: Battery Life Predictions

Built a medical equipment’s battery remaining life prediction system leveraging Machine Learning models based on early life cycle test data. The model predicted the remaining life in terms of the number of cycles.



The customer wanted to develop an AI-powered system to predict the remaining life of the batteries used in their medical equipment or products. Battery life is defined by the number of cycles of charge and discharge. Current testing mechanisms for battery remaining life are time-consuming and cost-prohibitive. They asked ThirdEye to build a predictive system to save them time and money.


ThirdEye developed a Machine Learning  Model based on early life cycle test data. The model predicted the remaining life in terms of the number of cycles. The model was trained on the latest data, arriving at regular intervals to increase the prediction accuracy to 90%. It helped the customer to take necessary actions proactively.

ThirdEye developed the Machine Learning Model and trained the model successfully on the early life cycle test data. Currently, the model predicts the remaining battery life with 75% accuracy. The team is working on the model to increase the accuracy percentage.

The process steps were as follows:

  • Performed Feature processing, including feature selection and feature engineering
  • Training and testing regression models.
  • Multiple model types were tried.
  • A reinforcement learning-based system was used to guide the testing process
  • Took an Active Learning approach to judiciously decide on the batteries further.

Technologies Used:

  • Data exploration Python packages
  • Tensorflow
  • SciKit Learn for non deep learning solutions
  • Python programming language
  • MySQL


ThirdEye has successfully tested and deployed the model with the given early life cycle test data. The customer has also tested the system and validated it as per the stated business requirements. The customer has accepted that the prediction model is delivering the desired results. The customer has requested ThirdEye to work further on the model to make it ready to be deployed in the operation with a higher accuracy percentage.

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