Predictive maintenance notifications based on sensor data

Purpose

1.1. Automate the delivery of predictive maintenance notifications for water filtration plant operations, leveraging sensor data analytics to reduce downtime, increase asset longevity, and improve maintenance efficiency.
1.2. Centralize real-time monitoring of industrial IoT sensor readings, automate anomaly detection, and automate the initiation of scheduled or ad-hoc maintenance alerts to maintenance teams or external service providers.
1.3. Automates seamless, scalable, and cross-platform alerting workflows across multiple corporate locations in compliance with utility industry regulations and enterprise standards.

Trigger Conditions

2.1. Sensor data anomalies such as sudden pressure drops, increased turbidity, temperature or vibration changes, or persistent deviation from baseline values.
2.2. Predictive analytics algorithms detecting increased probability of equipment failure or component wear.
2.3. Maintenance interval thresholds are exceeded for pumps, filters, or valve assemblies.
2.4. Automated error logs or downtime events generated by SCADA or plant monitoring software.
2.5. Automated requests by AI/ML predictive maintenance models.

Platform Variants

3.1. AWS IoT Core
• Feature/Setting: Rules engine—automate SNS topic trigger on anomaly; configure RuleAction with sensor stream.
3.2. Microsoft Azure IoT Hub
• Feature/Setting: Automate Event Grid; configure Event Subscription for device telemetry; automate Logic App workflow.
3.3. Google Cloud IoT Core
• Feature/Setting: Pub/Sub payload trigger; automate Cloud Function firing for notification; configure subscription filters.
3.4. IBM Maximo
• Feature/Setting: Condition Monitoring rule; automate automated work order creation upon threshold breach.
3.5. Siemens MindSphere
• Feature/Setting: MindConnect API; automate Asset Health Event; configure notification recipient.
3.6. GE Predix
• Feature/Setting: Time Series Analytics; automate rule-based notification automator; set Webhook for dispatch.
3.7. PTC ThingWorx
• Feature/Setting: Alerts & Events service; automate configuration for predictive maintenance triggers and recipients.
3.8. Splunk
• Feature/Setting: Real-Time Search Alert; automate webhooks on custom index query matches.
3.9. PagerDuty
• Feature/Setting: API trigger_incident; automate escalation policy and services.
3.10. Twilio
• Feature/Setting: Programmable Messaging API; automate send SMS for alert; configure webhook for incoming triggers.
3.11. SendGrid
• Feature/Setting: Mail Send API; automate maintenance alert email with incident payload.
3.12. ServiceNow
• Feature/Setting: Automated Incident Management module; configure API POST rule for new incident from alert.
3.13. Slack
• Feature/Setting: Incoming Webhooks for automated channel notifications; configure sensor data payloads.
3.14. Microsoft Teams
• Feature/Setting: Automated Flow with Power Automate; configure Teams message posting for event triggers.
3.15. Zapier
• Feature/Setting: Webhooks trigger; automate sequence for filtering, format, and notification.
3.16. InfluxDB
• Feature/Setting: Kapacitor TICK script; automate alert node for custom maintenance logic.
3.17. Node-RED
• Feature/Setting: MQTT Input node with automated function flows for alert logic and notification nodes.
3.18. Grafana
• Feature/Setting: Alert notification channel; automate maintenance notification via webhook or messaging.
3.19. Opsgenie
• Feature/Setting: Create Alert API; automate incident escalation and automated follow-up.
3.20. Salesforce
• Feature/Setting: Automated Case Creation via API or Flow Builder automation; route to appropriate team.
3.21. Google Sheets
• Feature/Setting: Apps Script trigger; automated log entry and automated email on threshold breach.
3.22. Trello
• Feature/Setting: Automated Card Automation via API; add action card for each new maintenance event.

Benefits

4.1. Automates early fault detection and maintenance intervention, minimizing unplanned downtime.
4.2. Automated notification flows increase operational transparency and auditability.
4.3. Automator-driven escalation ensures timely response, streamlining the maintenance process.
4.4. Automating the entire workflow improves regulatory compliance and data integrity.
4.5. Cross-system automation facilitates standardized and scalable predictive maintenance processes enterprise-wide.