PROGRESS DataRPM
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PROGRESS DataRPM USP

DataRPM Cognitive Anomaly Detection and Prediction (CADP) is a comprehensive solution for achieving asset failure management that helps to enhance overall productivity. It uses meta-learning technology to automate predictions of asset failures. This ensures users to leverage the deluge of data generated by their machines to gain accurate insights into predicting asset breakdowns. Also, field technicians can take well-informed and proactive decisions to ensure minimal disruptions on the production floor. All this is possible by automating cognitive predictive maintenance, a process that is beyond human management.

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Summary

The CADP solution allows industries and businesses to autonomously detect and predict most of the seemingly random failures that occur on the production floor for any asset-based industry. This requires modeling for each asset, and the capability to differentiate the normal from the anomaly. When there are hundreds of thousands of assets to consider, building that many models manually are impossible. CADP’s cognitive capabilities accomplish this by teaching machines to learn, much like data scientists would. This is where the concept of meta learning is applied, which empowers machines to digitally capture their experience in running machine learning experiments on data and learn from that to build faster, better predictive models for every asset in an autonomous manner. The CADP solution connects machine sensor data to gain predictive insights which help to predict asset failures before they occur using predictive analytics. It also enhances asset reliability by eliminating emergency incidents thereby reducing maintenance costs and optimize operations with cognitive intelligence.

360 Quadrants

Strengths and Weaknesses
  • +8 Feature and Functionality
  • +9 Scalability
  • +10 Support and Services
  • +7 Breadth and Depth of Product Offerings
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