Internal Reporting & Performance MonitoringPurpose
1.2. Monitor telemetry streams to detect potential equipment failures before they occur, minimizing downtime.
1.3. Integrate cross-platform alerting, ticketing, visualization, and reporting for proactive service scheduling and internal performance tracking.
1.4. Deliver actionable insights and automated work orders for field engineers and account managers.
Trigger Conditions
2.2. Predictive model signals an impending failure based on trend deviation or threshold breach.
2.3. Scheduled cyclic analysis (e.g., every 12 hours) on telemetry datasets for anomaly scoring.
2.4. Unfinished maintenance flagged in previous cycle or completion signal missed.
Platform Variants
3.1. Microsoft Azure IoT Hub
• Feature/Setting: Device Telemetry Stream Analytics; configure Event Hub ingestion for compressor telemetrics.
3.2. Google Cloud IoT Core
• Feature/Setting: Data routing to Pub/Sub for anomaly event triggering; set up Predictive Maintenance Model API endpoint.
3.3. AWS IoT Analytics
• Feature/Setting: Pipeline to AWS Lambda and SageMaker endpoint; use ‘CreateDatasetContent’ and ‘InvokeEndpoint’ APIs for inference.
3.4. Siemens MindSphere
• Feature/Setting: Insights Hub Monitor asset anomaly alerts; use ‘Event Management’ for workflow integration.
3.5. IBM Maximo
• Feature/Setting: Predictive Maintenance; set predictive asset rules and connect with Watson APIs for insights.
3.6. Splunk
• Feature/Setting: Data ingestion (HTTP Event Collector); setup anomaly dashboards, automate alerting with ‘saved search’ triggers.
3.7. ServiceNow
• Feature/Setting: Incident creation via REST API from analytics results; configure automatic assignment/routing.
3.8. PagerDuty
• Feature/Setting: Event orchestration; use ‘Events API v2’ to trigger on-call alerts for maintenance incidents.
3.9. Twilio SMS
• Feature/Setting: SMS alert API; send predictive maintenance notifications to technicians by POST request.
3.10. Slack
• Feature/Setting: Incoming Webhooks for maintenance alerting; configure channel for compressor event notifications.
3.11. SendGrid
• Feature/Setting: Automated email notification via SMTP or Web API when predictive failure detected.
3.12. Tableau
• Feature/Setting: Live dashboards for compressor health; connect via Web Data Connector or REST API.
3.13. Power BI
• Feature/Setting: Real-time dashboard using DirectQuery to cloud databases with predictive analytics visuals.
3.14. Salesforce Service Cloud
• Feature/Setting: Auto-create maintenance case via API from predictive alert with field assignment.
3.15. Zoho Creator
• Feature/Setting: Custom app with API webhook input for predictive alerts, task management triggered on event.
3.16. Airtable
• Feature/Setting: Automation script to append predictive maintenance events as records; trigger workflow when new entries added.
3.17. Jira Service Management
• Feature/Setting: REST API integration to auto-create tickets from anomaly detection; assign to maintenance queue.
3.18. Monday.com
• Feature/Setting: Automation via webhook/API to create/update items when predictive issues found.
3.19. HubSpot
• Feature/Setting: Workflow automation to email relationship manager when key customer compressors flagged at risk.
3.20. Notion
• Feature/Setting: Use API to create/update knowledge base pages for recurring compressor issues, tie to maintenance alerts.
3.21. Asana
• Feature/Setting: Task automation via API based on incoming predictive maintenance events, assign and track progress.
3.22. Microsoft Teams
• Feature/Setting: Automated channel messages from webhook whenever a predictive maintenance issue is identified.
3.23. Snowflake
• Feature/Setting: Store historical event and anomaly data; trigger SQL-based pipeline for machine learning updates.
Benefits
4.2. Streamlines internal reporting and supports data-driven performance review.
4.3. Improves technician productivity through automated actionable alerts and task routing.
4.4. Enhances customer satisfaction with proactive service and transparency.
4.5. Increases operational visibility and long-term asset reliability via integrated analytics.