What’s new in a-Gnostics 2.0. Industrial AI service focused on anomaly detection and equipment failure prediction

Industrial AI service focused on anomaly detection and equipment failure prediction

Background

Failure Prediction service at a-Gnostics DataDome
  1. Forecast electricity consumption by regions and counties, accuracy 96–99%.
  2. Forecast energy resources (electricity and natural gas) consumption by large factories, accuracy ~95%.
  3. Forecasts of solar (PV) stations generation, accuracy up to 90%.
  4. Failure prediction and anomaly detection service for industrial equipment. Predictive analytics for the boilers at thermal power plants.

Introduction

Top-level a-Gnostics architecture
  1. Storage;
  2. DataOps;
  3. ModelOps;
  4. API.

Data

  1. historical data — most of them are csv, excel, and txt files, in addition, there may be files of a specific format, depending on the domain area. For specific formats, it is necessary to develop separate processing modules;
  2. real-time data — usually, these are values from equipment sensors. In this situation, there is no single standard, in most cases, needs to deal with an intermediate bridge in the cloud environment (AWS S3, AWS Kinesis, Azure Blob, Azure FileShare, etc.) or a separate standing server (for example, FTP), where the data located. The presence of such a link is dictated by security measures to protect against external interference. This is a definite plus, since there is no need to spend time developing custom connectors to handle specific protocols (for example, Modbus), although there are exceptions to the rules. The idea that an external user can connect using a conditional MQTT and start reading indicators is more an exception than a reality;
  3. open data — any useful information from open sources that will improve the training set or complement the visualization, for example, weather facts and forecasts;
  4. proprietary data — information that is not available in open sources or by the customer. In some tasks, needs to clarify open weather forecasts (cloudiness), or supplement them with new ones (solar radiation).

DataOps

Data Storage

  1. metainformation;
  2. raw data;
  3. proceed data;
  4. datasets ready for machine learning;
  5. models;
  6. models results’ — forecasts, failure prediction, etc.

Models

Several different forecasts of electricity consumption for the manufacturing industry

API

Security

Conclusion

--

--

--

a-Gnostics implements an Industrial AI service focused on anomaly detection and equipment failure prediction

Love podcasts or audiobooks? Learn on the go with our new app.

Recommended from Medium

Why AI is here to stay

Image result for robot ai

In the future, we could solve all crime. But at what cost?

How To Always Win A Card Game — Using Simple AI

QFIL Options raises $1 million in pre-Seed funding

AI : What is?Since when?

Financial Automation

What is AIOps? A Software Intelligence Approach

Reinforcement learning through self-play and populations of agents

Get the Medium app

A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store
A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store
a-Gnostics

a-Gnostics

a-Gnostics implements an Industrial AI service focused on anomaly detection and equipment failure prediction

More from Medium

Understanding Federated Learning

MindMaker AI Plugin for Unreal Engine 4 & 5: Blueprint Functions

Generating Golf Clubs with StyleGAN2-ADA : I created the new golf driver design !

The golden rules of crowdsourced labelling