Progress DataRPM enables R&D and innovation teams to achieve faster time-to-insight, improved uptime, quality, yield and maintenance of industrial assets
The flood of data coming from sensors on industrial equipment gives asset-intensive organizations a tremendous opportunity to prevent failures and optimize output. However, industrial organizations globally are struggling to make sense of their data and to detect anomalies and prevent failures that otherwise often go undetected until costly failures have already occurred. With anomaly detection and prediction capabilities within the Progress DataRPM application, asset-intensive companies can unlock the power of the IIoT to capture and analyze their own industrial sensor data privately and securely to dramatically reduce downtime and increase overall equipment effectiveness.
The self-service option in the R&D license empowers R&D and innovation groups of industrial companies to leverage the fully automated machine learning anomaly detection and prediction capabilities within the DataRPM application, transforming their raw sensor data into intelligent actions for multi-sensor time series data analysis. R&D teams can start accurately detecting and predicting anomalies across their industrial data to minimize equipment downtime while maximize overall output. They can derive higher true positives and lower false positives with accurate insights to take timely actions to reduce unplanned downtime, unscheduled maintenance and to better control assets.
Through Progress DataRPM anomaly detection and prediction option, industrial decision makers, data scientists, heads of Innovation, R&D and machine learning and big data decision makers now have access to:
- Self-Service: End-to-end automation of the steps from data ingestion and analysis to insights visualization. Users can easily upload sensor data, map the attributes and click “run”. The entire cognitive flow works in a fully automated fashion to show near-immediate results.
- Smart Insights: The results are shown in ”stories,” in a human-readable format that highlights patterns and anti-patterns in the sensor data.
- Exploratory Analysis: Using drill-down and filters, users can gain a better understanding of the behavior of assets and most important sensors for predicting the most likely failures states.
- Enterprise-grade Data Science Process Flow Framework: For those with successful POCs and pilots, the framework enables a seamless transition from R&D to full production environments with no code rewrites.
“With billions of interconnected devices pumping out untold volumes of
data, there is a huge demand for ways to gather valuable insights from
the data. But with limited budgets and lengthy deployment cycles for
many machine learning applications, the true value of data is often left
untapped or underutilized,” said Dmitri Tcherevik, Chief Technology
The Progress DataRPM application uses cognitive techniques and advanced machine learning and meta learning-based algorithms to identify and predict anomalies, often before they occur in the production environment. Meta-learning, a subset of machine learning, is a set of algorithms that teach computers how to self-learn in difficult Industrial IoT big data environments. DataRPM anomaly detection and prediction option provides fast, repeatable, scalable and highly interpretable results by analyzing highly complex sensor data in minutes, reducing equipment failures and increasing output quality and yield.
For more information about Progress DataRPM, go to www.progress.com/datarpm.
Kim Baker, +1 888-365-2779