Robotic Arm Failure Prediction Using AI Data Analytics｜RapidMiner

       The customer is a parts manufacturer. In order to reduce labor costs and improve efficiency, a large number of robotic arms have been introduced in the factory to assist with material handling. To avoid production downtime caused by robot failures, they hope to use AI big data analytics to predict failures in advance and thereby plan appropriate maintenance schedules for the robotic arms.
 

.......By using Knowledge Studio and RapidMiner, based on the collected robot data, including operating time, application, drive type, arm length, arm type, etc., an AI prediction model was trained. This prediction model is used to predict robotic arm failures and ultimately establish a complete maintenance schedule.

 

• For the need of robotic arm failure prediction, through Knowledge Studio and RapidMiner, the correlation between different factors and failures can be observed.

• Based on the data, an AI model is built to achieve robotic arm failure prediction.

• By introducing KPIs, management can prioritize maintenance tasks and achieve optimization of maintenance and service workflows.

• AI big data analytics provides many algorithms, such as clustering (K-means), K-NN classification, association analysis, and supervised learning methods (decision trees, random forests, deep learning, bagging, linear regression, etc.) to build prediction models.