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Edge-AI Powered Predictive Maintenance for Industrial IoT Using AWS Cloud Services

Edge-AI Powered Predictive

Edge-AI Powered Predictive Maintenance for Industrial IoT Using AWS

  • Use Case: In manufacturing plants, industrial machinery, and smart factories, unexpected equipment failures cause downtime and financial loss. Using Edge-AI and IoT, predictive maintenance can detect anomalies, predict failures, and schedule maintenance proactively, reducing operational costs and downtime.

Objective

  • Implement an Edge-AI system for predictive maintenance of industrial equipment.

    Collect real-time sensor data (vibration, temperature, pressure) from IoT devices.

    Use machine learning models to detect anomalies and predict equipment failures before they occur.

    Enable local decision-making at the edge for fast response, while leveraging cloud resources for model training and analytics.

Project Description

  • This project proposes a predictive maintenance solution where industrial IoT devices are connected to AWS IoT Core and managed via AWS IoT Greengrass V2 for local computation.

Data Collection & Edge Processing

  • IoT sensors collect vibration, temperature, and pressure data.

    Greengrass edge devices run lightweight ML models to detect early signs of failure in near real-time.

Cloud-Based Model Training & Updates

  • Historical sensor data is sent to Amazon SageMaker to train and refine predictive maintenance models.
  • Updated models are deployed back to edge devices via Greengrass components.

Monitoring & Alerts

  • CloudWatch monitors device health and system performance.
  • SNS/EventBridge sends alerts to maintenance teams if anomalies or potential failures are detected.
    • Key Technologies & AWS Services :
    Category AWS Service / Technology Purpose
    IoT Connectivity AWS IoT Core Securely connect industrial IoT sensors and devices.
    Edge Processing AWS IoT Greengrass V2 Deploy ML models and process sensor data locally for fast anomaly detection.
    Machine Learning Amazon SageMaker Train predictive maintenance models on historical IoT data.
    Data Storage Amazon S3 Store sensor data, logs, and model artifacts.
    Monitoring Amazon CloudWatch Track sensor metrics, edge device performance, and ML inference results.
    Notifications Amazon SNS / EventBridge Alert maintenance teams on potential failures or anomalies.
    Security & Access AWS IAM Manage secure access to devices and cloud resources.
    Local Communication Greengrass Local MQTT Broker Facilitate low-latency communication between edge devices.