machine-data-iiot

Cognitive Anomaly Detection and Prediction: The Key to Operational Excellence

Using and profiting from the flood of sensor data coming from every machine used to be out of reach—until now.

The Challenge

Transforming Raw Sensor Data Into
Intelligent Action

Your machine assets generate massive amounts of data with the potential for extraordinary business value.

Extracting value from such a huge volume of data is beyond human management—what's needed is a cognitive machine learning solution.

The Solution

Cognitive Anomaly Detection and Prediction

The Progress® Cognitive Predictive Maintenance Solution automates data science enabling asset-intensive organizations to gain exceptional control over the torrent of sensor data coming from every machine. The automated approach uses a patented meta-learning solution to detect and predict anomalies, deliver machine health insights, reduce the time required to develop and operationalize models and help data scientists be more effective.

The solution lets you:

Identify Unknown Errors

80% of machine failures come from unknown errors. Cognitive predictive maintenance helps find them.

Analyze Every Asset

A built-in digital twin model accounts for environmental, operational and manufacturing factors.

Increase Data Science Effectiveness

Focus your valuable data science efforts on strategic analysis vs. managing the repetitive tasks required to develop analytical models.

Run Complex Workloads

Distributed processing lets you run complex workloads at scale while reducing the number of modeling experiments needed to devise highly accurate models.

reactive
Whitepaper

Time to Relook at Asset Reliability- Move from Reactive to Predictive

Did you know that the failure of critical assets continues to be the number one operational risk for leading industrial outfits across the world?

Read the Story
The Business Benefit

Maximize Uptime, Optimize Yield and Improve Quality

Detect, track and act upon asset anomalies and boost Overall Equipment Effectiveness (OEE).

90%
reduction in unplanned downtime
20%
optimization of inventory cost
35%
boost in overall equipment effectiveness
35%
increase in asset life
25%
improvement in output quality
50%
increase in an asset's operation efficiency

Why Progress for Predictive Maintenance?

Superior Prediction Accuracy

Partial data sets and generalized models significantly reduce prediction accuracy.

Progress analyzes all machine data to uncover anomalies before they become failures.

Maximize Equipment Effectiveness

Existing approaches detect known, repeated conditions, accounting for only 20% of equipment failures.

Progress detects both repeated and random failures, representing the remaining 80%.

Deliver in Days

Traditional solutions take months to implement and tie up valuable data science resources.

Progress delivers results in days with minimal data science effort.

gas-pipeline-iiot
Success Story

How IIoT helped Transform a Gas Pipeline

DataRPM’s Cognitive Anomaly Detection and Prediction (CADP) let the client identify gas pipeline stations with the highest contribution to pipeline performance and the reasons for varying performance between individual stations.

Read the Story

Products

The Progress Cognitive Predictive Maintenance Solution is powered by DataRPM Cognitive Anomaly Detection and Prediction (CADP)

DataRPM

Cognitive Predictive Maintenance for Industrial IoT

By harnessing the power of machine learning, asset intensive organizations gain exceptional control over the torrent of sensor data coming from every machine.

Additional Resources & Learning

new-trend-4
webinar

Future Proofing Asset Failures with Cognitive Predictive Maintenance

The industry is reeling under the explosion of data generated by smart sensors, motors, actuators, machines, and other “things”.

new-trend-1
whitepaper

Time to Relook at Asset Reliability – Move from Reactive to Predictive

Did you know that the failure of critical assets continues to be the number one operational risk for leading industrial outfits across the world?

Forrester Wave
Blog

How to Put Machine Learning to Work for Your Business

What are the steps you should take to be ready to take advantage of Machine Learning, and why is now the time to do so? Learn the answers in part two of our series on demystifying Machine Learning.

4th Annual Data and Analytics Research Study
ebook

Asset Reliability: From Hiccuping to Humming Production Floors

Digitization has placed immense pressure on businesses and the way they function. Organizations are struggling to cope with not only increasingly sophisticated machines, but also the data these machines generate.

predictive-maintenance

Progress Cognitive
Predictive Maintenance Solution

Detect and predict anomalies by automating machine learning to achieve higher asset uptime and maximize yield.