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Progress Launches AWS-Backed IIoT Anomaly Detection and Prediction Service

Progress announced on Thursday the launch of a new self-service anomaly detection and prediction option for the IIoT market that leverages AWS.

Written By

David Curry

Jun 2, 2018

Application development and deployment company Progress announced on Thursday the launch of a new self-service anomaly detection and prediction option for the Industrial Internet of Things (IIoT) market.

The service is able to look at the wealth of data coming from IIoT sensors and recognize failures or sub-optimal output, potentially reducing downtime by 90 percent and increasing asset time by 35 percent.

Progress DataRPM is applicable to multiple industries, including manufacturing, oil and gas, automotive, aviation, logistics, and energy. In oil and gas, Progress offers a 100 percent reduction in decision errors and a 75 percent reduction in offshore asset breakdown.

The service is hosted on Amazon Web Services (AWS), and the cloud computing giant will offer free trials to qualified manufacturers with connected sensors.

DataRPM is provided within a new R&D specific license, which allows R&D and innovation groups to turn raw sensory data into meaningful analysis on assets. Progress believes the solution will empower those groups with better decision-making capabilities for pilot execution and IIoT proof of concept.

“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 at 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,” he added.