To reach the goal of continuously predicting a machines conditions / the remaining useful life, the prediction models have to be applied in real-time to your data. Our module inCARE automates this task for you informing staff if and when maintenance is required.

The module IPN inCARE is used to predict the point in time at which an incident is going to occur (“Remaining Useful Life”). To do so, a real-time model for every machine monitored is deployed which predicts the incident / the machine state based on specific variables of influence. The predictions are calculated based on process and status data that are collected and pre-processed by the modules IPN inPOLL and IPN inPREP.

inCARE covers the phases “Modeling”, “Evaluation” and “Deployment” of CRISP-DM as well as the continuous application of the models to your real-time data in order to provide insights about your machines actual and future condition at any point in time.

The models used for predictions can be provided and loaded into inCARE both by IPN and by the user himself. In case you do not plan to develop the predictive models yourself IPN is offering “model development” as a service. The model development is based on the IPN methodology which is the basis of all our consulting services.

The module inCARE is mainly used to predict equipment maintenance events as well as short term equipment failures. In predictive maintenance projects realised by IPN the module is used to predict the maintenance tasks of the cooling chamber of a steel casting plant (28days upfront) or to predict short-term equipment failures of firefighting cars and entrance systems (several minutes to hours upfront).