The maintenance of industrial robots is a critical issue in manufacturing processes, and it can have a significant impact on productivity, downtime, and costs. To minimize the risks of failures and downtimes, predictive maintenance is becoming a popular approach in industry. Predictive maintenance aims to anticipate equipment failures before they occur, allowing for scheduled maintenance and reducing unexpected downtime. 
Predictive maintenance is a crucial aspect of modern manufacturing processes, and the development of a predictive maintenance system for industrial robots can significantly improve productivity and reduce downtime. By analyzing the power measurements of the robot and using machine learning algorithms, the proposed system can identify accuracy errors and predict potential failures. This has the potential to provide valuable insights into the predictive maintenance of industrial robots and improve the overall efficiency of manufacturing processes. 
Predictive maintenance is a strategy that uses sensors and data analysis to predict when equipment is likely to fail or require maintenance. 
Robotic predictive maintenance involves using robots to perform these tasks, which can be more efficient and safer than relying on human workers.  
The main advantage of this approach is that it can help to minimize equipment downtime by identifying and addressing potential problems before they cause equipment failure. 

User Management

Different user-level support
C Levels GM/PM/Directors Managers Engineers Technician

All In One Platform

Collecting internal data from various types of industrial robots in one platform.

Predictive and Prescriptive Analytics

Avoid repair and downtime costs by detecting failures in advance.

Anomaly Detection

Define anomaly types
Collecting anomalies
Anomalies list with priority 
Convert anomalies to task
Listing solutions added by users

Alert Management

Web notification
Microsoft Teams
See the task detail page.