The Product Condition Prediction service aims to support the de- and remanufacturing processes by providing immediate condition prognosis for second-life products, thus aiding circular economy. The main functionality of the service is to provide a catalog of predefined models in order to predict different properties related to the product condition in different stages of the product’s lifetime.
The models of the catalog are built utilizing Machine Learning and other AI algorithms. Each model solves a prediction scenario in a form of inputs and outputs, which describe the source information (input properties like product specification, test results, in-use history data) and the prediction target (output properties indicating the product condition).
For the user:
- Product condition indicators (like State of Health) for the provided second-life products, considering the input requirements of available models
- Display and file export options of the predicted product condition indicators
For the platform:
- Optional upload functionality for the predicted product condition indicators, expanding the available information on the products