Development of an Anomaly Detection and Predictive Maintenance System for Manufacturing Equipment

The manufacturing sector is experiencing rapid operational shifts due to retiring experienced personnel and evolving machinery. Companies require scalable, sustainable solutions to accurately predict machine failures, reduce unplanned downtime, and maintain competitive edge without relying solely on human expertise.

A global manufacturer of industrial machinery seeking to enhance equipment reliability and reduce downtime through intelligent monitoring and predictive analytics.

• Deliver a robust anomaly detection platform capable of predicting machinery failures prior to occurrence.
• Reduce unplanned machine downtime by implementing real-time alerts and diagnostics.
• Enable comprehensive monitoring of machine health metrics and aggregate performance over time.
• Provide administrative control features such as user management, alert customization, and API access.
• Achieve a deployment timeline of approximately 5-6 months with scalable infrastructure.

• Edge computing modules interfacing with existing industrial PLCs via standard industrial protocols (CIP over EtherNet/IP) for real-time data collection.
• Cloud backend infrastructure supporting data ingestion, storage, and processing using scalable serverless solutions such as AWS Lambda and AWS Kinesis.
• Machine learning models for detecting anomalous behavior and predicting potential failures based on collected data.
• Rich dashboards displaying real-time machine health metrics, anomaly alerts, and historical aggregate data.
• User management and system configuration options, including alert preferences, user roles, and API integrations.

• Industrial PLCs via CIP over EtherNet/IP for data acquisition
• Cloud data pipelines for real-time data processing and storage
• Security protocols and API endpoints for system administration and external access

• System scalability to support increasing numbers of machines and data volume
• Real-time data processing latency under 1 second for anomaly alerts
• High system security including secure data transmission and access controls
• System availability with 99.9% uptime to ensure reliable operations

Implementation of this anomaly detection and predictive maintenance system is expected to significantly reduce unplanned equipment downtime, improve machine reliability, and enhance operational efficiency. Anticipated outcomes include a reduction in downtime by approximately 20%, increased machine lifespan, and providing a scalable platform to support future industrial IoT innovations.