Predictive maintenance
Predictive maintenance analytics captures the condition of industrial equipment so you can identify potential breakdowns before they impact production.
Businesses are constantly seeking faster ways to take advantage of the value of sensor-based information and transform it into predictive maintenance insights that people can act on quickly.
Predictive maintenance insights provide valuable services, such as predicting equipment failure, real-time anomaly detection, predicting pressure spikes, and asset health monitoring.
Solutions
Software AG
Zementis from Software AG enables immediate deployment of analytic models within AWS IoT environments. Data scientists often develop models in a sandbox environment, then work with IT to deploy each model. Zementis on AWS IoT eliminates that requirement.
Zementis leverages open industry standards, allowing users to bring both existing and new models into their AWS environments without custom coding. This framework can be used across a variety of use cases in IoT applications.
Here’s how it works:
- When AWS IoT receives a message, it authenticates and authorizes the message and the Zementis Rules Engine executes an appropriate rule on the message to determine whether an AWS Lambda function should be invoked or not.
- Certain messages invoke an AWS Lambda function and analytics to determine if there is an anomaly in the data that would trigger further analysis on a particular machine in a factory. The results of these analytics are stored in an Amazon DynamoDB (DynamoDB) table.
- In case further analysis is required, an equipment health monitoring model or models are executed by the Zementis Server on AWS to determine what the next action should be. This triggers an Amazon Simple Notification Service (Amazon SNS) notification regarding the next step of the monitoring and maintenance process for the particular machine and/or the system. The results of these analytics are stored in a DynamoDB table.
- The equipment health monitoring IoT model deployed by Zementis invokes an AWS Lambda function that processes equipment health data during a specified time period and stores it in a DynamoDB table.
- The equipment health score AWS IoT rule detects the end of a time period and invokes a Zementis deployed model that processes aggregate equipment health data to generate an equipment health score, trigger an Amazon SNS notification to the designated user(s), and add the score to the historic equipment health table.
AWS customers who are easily and securely connecting devices to the cloud
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