1. Predictive Maintenance Scheduling with EventRuleEventSubscription


    Predictive maintenance scheduling is a strategy that monitors equipment condition and performance to predict when maintenance should be performed. This approach promises cost savings over routine or time-based preventive maintenance because tasks are performed only when warranted.

    To implement predictive maintenance scheduling in a cloud infrastructure, you might leverage various services and tools. Here, we can create an event-driven architecture that listens for signals or metrics indicating that a piece of equipment is likely to need maintenance soon.

    In AWS, one might use Amazon CloudWatch Events (known as EventBridge since mid-2019) to set up rules that match incoming events and route them to one or more target actions, such as invoking an AWS Lambda function or placing a message in an Amazon SNS topic. This function or topic can then trigger the necessary maintenance workflows.

    Below you'll find a Pulumi Python program that sets up a predictive maintenance scheduling system using AWS EventBridge and an SNS topic. The EventBridge rule is scheduled to run periodically and checks for any events that match certain criteria, such as error rates exceeding a threshold:

    import pulumi import pulumi_aws as aws # Create an SNS topic that will receive notifications maintenance_notifications_topic = aws.sns.Topic("maintenanceNotificationsTopic") # Create an EventBridge rule that runs on a fixed schedule and looks for signs that # predictive maintenance is required, such as an error rate metric exceeding a threshold predictive_maintenance_rule = aws.cloudwatch.EventRule("predictiveMaintenanceRule", schedule_expression="rate(1 day)", description="A rule that triggers predictive maintenance scheduling based on certain signals." ) # Set the EventBridge rule to publish to the SNS topic when triggered event_subscription = aws.cloudwatch.EventSubscription("eventSubscription", rule=predictive_maintenance_rule.name, arn=maintenance_notifications_topic.arn ) pulumi.export("maintenance_topic_name", maintenance_notifications_topic.name) pulumi.export("maintenance_rule_name", predictive_maintenance_rule.name)

    This program will deploy the following resources:

    1. aws.sns.Topic: An SNS topic named maintenanceNotificationsTopic, which will be used to send notifications about equipment that may need maintenance.
    2. aws.cloudwatch.EventRule: An EventBridge rule named predictiveMaintenanceRule that includes a schedule_expression to trigger the rule once a day. This placeholder rule should be adapted to include the actual logic for predictive maintenance.
    3. aws.cloudwatch.EventSubscription: A subscription that connects the predictiveMaintenanceRule with the maintenanceNotificationsTopic. Whenever the rule is triggered, a message is sent to the SNS topic.

    The provided rule is set to trigger once a day with rate(1 day) as the schedule expression. You will need to modify the predictive_maintenance_rule with actual conditions or patterns that match the logs or machine metrics indicated in need of predictive maintenance.

    Lastly, the program exports the names of the SNS topic and EventBridge rule so you can easily reference them in the AWS console or in other parts of your infrastructure.