Smart Industrial Sensor Network for Predictive Maintenance

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Smart Industrial Sensor Network for Predictive Maintenance

SiriusOne helped a leading manufacturer enhance maintenance with an AI-powered IoT solution that monitors equipment health, detects failures early, and automates predictive maintenance. The system improved efficiency, reduced downtime, and optimized resources.
Tech Stack: AWS IoT Core, FreeRTOS, Python, MQTT, Edge AI, DynamoDB, AWS Lambda, Amazon SageMaker
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Client & Project Overview:

Anindustrial manufacturer faced recurring unplanned equipment failures, which led to costly emergency repairs, frequent production stoppages, and inefficient maintenance workflows. The company required a real-time IoT-based solution that could:

  • Continuously track key performance indicators (KPIs) such as vibration levels, temperature, and pressure.
  • Predict potential failures before they happen using machine learning.
  • Automate maintenance scheduling to optimize resource utilization and extend equipment lifespan.
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Business Challenge:

The manufacturer struggled with:

  • High Operational Downtime - Frequent unplanned outages due to undetected early-stage failures.
  • Reactive Maintenance Approach - Maintenance teams operated on a break-fix model rather than a proactive strategy.
  • Lack of Data-Driven Decision-Making - Manual inspections failed to provide predictive insights, leading to suboptimal maintenance scheduling.

Solution:

SiriusOne developed a comprehensive IoT-powered predictive maintenance ecosystem that transformed the client’s operations:

1. Smart Sensor Network

  • Installed industrial-grade vibration, temperature, and pressure sensors on critical equipment.
  • Enabled secure, real-time data transmission via MQTT to AWS IoT Core.

2. AI-Driven Edge Computing

  • Deployed Edge AI models running on FreeRTOS-based microcontrollers, enabling local processing of sensor data.
  • Implemented anomaly detection algorithms that flagged irregular equipment behavior in real time.

3. Cloud-Based Predictive Maintenance Platform

  • Leveraged Amazon SageMaker to build machine learning models that forecast equipment failures.
  • Developed a dashboard with real-time analytics, automated alerts, and predictive maintenance scheduling for operational managers.

Results:

  • 40% Reduction in Downtime - Improved production uptime by proactively addressing maintenance needs.
  • 25% Lower Maintenance Costs - Reduced unnecessary repairs and resource wastage.
  • Extended Equipment Lifespan - Optimized asset utilization through data-driven insights.

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