Boost Efficiencies by Reimagining IIoT Data

Leverage powerful machine learning analytics to tackle complex industrial data

Progress Anomaly Detection and Prediction: A Machine First-Solution for Your Industrial IoT Data

Progress DataRPM Anomaly Detection and Prediction (ADP) identifies problem signals early to help you avoid unplanned downtime, expensive maintenance, high risks and cost impact due to decreased efficiency, yield or quality. It does it through automated machine learning on time-series data coming from one or more sensors, learning the various patterns of normal behavior and detecting the deviations that signal not just known problems, but also unknown problems.

ADP improves the productivity of the “4Ds”—data scientists, developers, designers and dev-ops—and enables them to work together on an enterprise grade platform that prevents rework, and transitions seamlessly from R&D to production to accurately detect and predict anomalies for assets individually and at scale.

Wipe Out Industrial Asset Health Problems in Just Six Steps

Connect

Upload CSV data or use connectors to structured data sources to provide data in a prescribed format.

Detect

See anomalies at asset/entity level as well as sensor/attribute level. View summaries and anomaly detection quality metrics.

Verify

Validate with known anomalies or events data.

Predict

Get predictions of anomalies and known/unknown events at asset/entity level as well as sensor/attribute level. View summaries and prediction quality metrics.

Visualize

See storylines of changes in asset behavior in real-time or historically before any failure event.

Act

Use APIs to connect into business systems to take action on the detected anomalies and predictions.

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A Blueprint for IIoT Success –
From Labs to Production at Scale

Progress ADP provides the fastest time to insight for R&D teams exploring ways to leverage their IIoT data to improve quality, yield and maintenance of their industrial assets. Our blueprint enables companies to leverage these opportunities to make the best decisions—from industrial IoT proof-of-concept planning and execution to production scale deployments.

The solution does the heavy lifting by analyzing machine data automatically and revealing insights in a user-friendly format that domain experts can act on.

Here’s how Progress ADP performs the key steps to automate the process:

Creates unique Data Signatures to identify similar and different assets and also the various stages each asset sensor goes through during the asset’s different operating phases.

Extracts Data Patterns to learn and model normal states, and then identify anomalous states—the "Anti-Patterns”—for each and every asset.

Trains an Ensemble of Models from similar assets and transfers learning across assets.

Applies the Ensemble of Models to predict issues for every asset.

Presents the insights from the data in an easily accessible and understood natural language format.

Extracts and Learns from metadata harvested from the data science workflow and improves Models continuously with feedback from domain experts.

Key Benefits

Higher Accuracy

Develop models faster with self-learning adaptive algorithms. Detect magnitude, sequence and frequency-based anomalies including the behavior within sensor stages, interaction between different sensors and the behavior of similar assets.

Better Prediction

Leverage unsupervised machine learning algorithms to learn about the early signals that indicate not just known problems, but also unknown problems.

Work With Unlabeled Data

Derive actionable insights from the readily available unlabeled data coming from sensors. Past failure data is not a prerequisite.

Scalable, Incremental and Dynamic Models

Eliminate manual data science efforts for individual assets. Automatically update production models as conditions change and the performance of predictive models deteriorates. Incrementally learn about and from the changes that a machine goes through.

Increased Productivity

Free data scientists from reinventing analytical workflows so they can focus on higher-value tasks. Designers, developers/data engineers and dev-ops can work with data scientists on an integrated platform that reduces rework and delays due to loss in translation.

Faster ROI

Run PoCs fast on smaller datasets while also deploying a massively parallel and distributed production scale solution with ease. Avoid duplication of efforts required beyond model building for constructing enterprise-grade data science production workflows.

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An Enterprise-Grade Framework for
Data Science Building Blocks

With Progress DataRPM, you can fast track your Industrial IoT proof-of-concepts easily. Cut down the unpredictability in your industrial data with our Data Science Building Blocks.

Recipes

Single data science task that combines methods and algorithms.
Build your own recipes out of the box in any language of your choice.

Meta-Learning Framework

Builds a digital experience repository by extracting metadata from various data science tasks and machinelearning experiments.
Learns from metadata to recommend optimal configurations and automate.

Process Flows

Combines multiple recipes to execute the tasks sequentially or in parallel in a single or distributed environment.
Go from research experiments to production scale applications easily.

Micro-App Framework

Build rich interactive user interfaces for a data product.
Customizable Micro-App is ready to use out-of-the-box.

Integration Framework

Ingest data from different sources using the connector framework.
Integrate results into external applications using RESTful framework.

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Learning & Resources

harnessing-your-machine-and-data-capabilities

Whitepapers

Explore research findings or tips about a certain business issue

Webinars

Watch past webinars about DataRPM on demand

Blogs

How-to articles, the latest news, training videos and industry trends

E-Books

Check out our latest ebooks on cognitive predictive maintenance

Solution Briefs

Learn more about our cognitive predictive maintenance solutions

Progress Anomaly Detection and Prediction

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