Automated anomaly detection in production data

Purpose

1.1. Automate continuous monitoring, analysis, and automatedly alerting for abnormal patterns or deviations in production process data.
1.2. Establish automation for identifying real-time anomalies in quality, yield, and resource usage, minimizing manual oversight.
1.3. Enable automating predictive controls to pinpoint equipment failures, material defects, or process bottlenecks with minimal human intervention.
1.4. Integrate automated anomaly detection across sensors, SCADA, MES, IoT, ERP, and production systems to automate root cause analysis and corrective workflow launches.
1.5. Ensure regulatory, quality, and compliance requirements by automating incident tracking and reporting when anomalies are detected.

Trigger Conditions

2.1. Automated trigger on sensor or PLC data stream crossing predefined thresholds.
2.2. Automation initiates when time-series variation deviates from trained ML model baselines.
2.3. Automated event start on receipt of error or outlier values via industrial API or data broker.
2.4. Auto-trigger upon batch, shift, or lot-end data review exceeding SPC control limits.
2.5. Exceptionally high or low statistical process metrics automatedly launch anomaly routines.

Platform Variants

3.1. AWS CloudWatch
• Feature/Setting: Anomaly Detection Alarms — Configure Metric Math with anomaly detection bands on production metrics.
3.2. Microsoft Azure Monitor
• Feature/Setting: Metric Alerts with Dynamic Thresholds — Automate alert on time-series deviation versus expected trend.
3.3. Google Cloud Operations Suite
• Feature/Setting: Alerting Policy with Detect Anomalies — Automate detection using built-in anomaly detectors for IoT metrics.
3.4. IBM Watson IoT
• Feature/Setting: Rules Engine — Configure anomaly detection rule with Watson AI signal analysis.
3.5. Siemens MindSphere
• Feature/Setting: Integrated Anomaly Detection Service — Automate notification for irregular machine patterns using MindConnect.
3.6. OSIsoft PI System
• Feature/Setting: Asset Analytics Formula — Automate alerts using expression-based detection on production tags.
3.7. GE Predix
• Feature/Setting: Time Series Anomaly Plugins — Configure and automate anomaly analytics on edge device data.
3.8. Splunk Industrial IoT
• Feature/Setting: Machine Learning Toolkit — Automate anomaly jobs on production event logs and data streams.
3.9. Honeywell Forge
• Feature/Setting: ML-based Asset Anomaly Detection — Configure automated event and alert creation on abnormal behavior.
3.10. Rockwell FactoryTalk Analytics
• Feature/Setting: Automated Model Builder — Train and automate anomaly detection models on real-time plant data.
3.11. PTC ThingWorx
• Feature/Setting: Analytics Server – Auto-detect outliers on asset monitoring applications, launching automated alerts.
3.12. InfluxDB
• Feature/Setting: Flux Queries — Automate continuous anomaly scan on time-series with stateful triggers.
3.13. Apache Kafka + KSQL
• Feature/Setting: Real-time Streams Filtering — Automate anomaly flagging in data ingestion pipelines.
3.14. Matlab Production Server
• Feature/Setting: Deploy Anomaly Models as Microservices — Automate requests to endpoint for real-time data scoring.
3.15. TensorFlow Extended (TFX)
• Feature/Setting: Automated Model Serving — Trigger inference and anomalies detection workflow in production.
3.16. Datadog
• Feature/Setting: Outlier Detection on Dashboard Widgets — Auto-generate alerts for abnormal process indicators.
3.17. Sentry (for Data)
• Feature/Setting: Automated Error Alerting — Integrate anomaly logging for production scripts and pipelines.
3.18. Keyence IoT Gateway
• Feature/Setting: Automated Anomaly Notifications — Integrate with PLC/machine for event-based alerts.
3.19. Ignition by Inductive Automation
• Feature/Setting: Scripting Events — Automate executing scripts in response to anomaly detection in real time.
3.20. SAP Manufacturing Integration and Intelligence (MII)
• Feature/Setting: Automated Alert Workflows — Configure rules to launch corrective actions when anomalies detected.

Benefits

4.1. Automates early detection and response, reducing downtime and waste.
4.2. Automates escalation and tracking, ensuring compliance with SOPs.
4.3. Enables more efficient resource allocation via automated insights.
4.4. Automating data-driven decisions leads to improved production quality and throughput.
4.5. Frees up valuable human resources for higher-value tasks through automated monitoring.