Progress with Amazon Web Services Offer Industry’s First Industrial IoT Self-Service Option for Anomaly Detection and Prediction using Cognitive Machine Learning

Progress DataRPM enables R&D and innovation teams to achieve faster time-to-insight, improved uptime, quality, yield and maintenance of industrial assets

Progress (NASDAQ: PRGS), the leading provider of application development and deployment technologies, today announced the availability of a new Progress® DataRPM® self-service anomaly detection and prediction option for the Industrial Internet of Things (IIoT) market. Provided within a new R&D-specific license, the first-of-its-kind offering will empower R&D and innovation groups with better decision-making capabilities for IIoT proof-of-concept (POC) and pilot execution. Hosted on Amazon Web Services (AWS), AWS will also offer free trials of Progress DataRPM cloud instances for qualified manufacturers with connected sensors and the ensuing time series data. The trial will allow companies to load their data securely on AWS, detect equipment anomalies, predict failures before they occur, and validate against failures – both known and unknown – thereby confirming pro-active steps that should be taken in advance to avoid unplanned downtime and unscheduled maintenance.

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 Officer, Progress. “That is why Progress now offers an R&D self-service option for those organizations looking to start on their IIoT journey more quickly and easily than previously possible. R&D teams can use our self-service cognitive cloud-based application to immediately start detecting and predicting anomalies across their industrial data for fast time-to-insights and more accurate ROA calculations.”

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.

Progress DataRPM enables R&D and innovation teams to achieve faster time-to-insight, improved uptime, quality, yield and maintenance of industrial assets

Progress (NASDAQ: PRGS), the leading provider of application development and deployment technologies, today announced the availability of a new Progress® DataRPM® self-service anomaly detection and prediction option for the Industrial Internet of Things (IIoT) market. Provided within a new R&D-specific license, the first-of-its-kind offering will empower R&D and innovation groups with better decision-making capabilities for IIoT proof-of-concept (POC) and pilot execution. Hosted on Amazon Web Services (AWS), AWS will also offer free trials of Progress DataRPM cloud instances for qualified manufacturers with connected sensors and the ensuing time series data. The trial will allow companies to load their data securely on AWS, detect equipment anomalies, predict failures before they occur, and validate against failures – both known and unknown – thereby confirming pro-active steps that should be taken in advance to avoid unplanned downtime and unscheduled maintenance.

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 Officer, Progress. “That is why Progress now offers an R&D self-service option for those organizations looking to start on their IIoT journey more quickly and easily than previously possible. R&D teams can use our self-service cognitive cloud-based application to immediately start detecting and predicting anomalies across their industrial data for fast time-to-insights and more accurate ROA calculations.”

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.