Industrial Data Lake for Predictive Maintenance using Amazon Monitron and Amazon Kinesis

Publication date: September 28, 2022 (Diagram history)

This architecture diagram shows you how to build a data lake using AWS IoT sensors, real-time data streams, alerts, visualization, and integrated workflow with Enterprise Resource Planning (ERP) to analyze factory data for predictive maintenance and improve equipment uptime.

Industrial Data Lake for Predictive Maintenance using Amazon Monitron and Amazon Kinesis Diagram

1. Install Amazon Monitron sensors and gateway in a factory.

2. Create Amazon Kinesis Data Streams using Amazon Monitron as the data source.

3. Configure Amazon Kinesis Data Streams from Amazon Monitron managed account to customer account.

4. Configure Amazon Simple Storage Service (Amazon S3) bucket as delivery destination of Amazon Data Firehose. Amazon S3 serves as storage foundation for industrial data lake.

5. Configure Amazon S3 notifications to send events to the Amazon EventBridge destination.

6. Configure an AWS Lambda function as the target of Amazon EventBridge destination rules. The Lambda function processes the Amazon S3 event and sends it to an AWS IoT Events state machine.

7. AWS IoT Events responds to sensor warning state and creates ERP work order using AWS Lambda.

8. AWS IoT Events responds to the sensor warning state and notifies personnel using Amazon Simple Notification Service (Amazon SNS) topic via SMS, mobile push, and email.

9. Connect AWS Glue data pipeline to Amazon S3 bucket and schedule Glue job via Amazon EventBridge. Amazon Athena then queries S3 data as reports and metrics.

10. Visualize IoT metrics and state from Athena queries using Amazon Managed Grafana.

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Change	Description	Date
Initial publication	Reference architecture diagram first published.	September 22, 2021

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