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IoT Sensor Data Analytics for Predictive Maintenance Using Google Cloud Dataflow and BigQuery ML

IoT Sensor Data

IoT Sensor Data Analytics for Predictive Maintenance Using Google Cloud

  • Use Case : Industrial plants, manufacturing facilities, and transportation systems rely on machinery and equipment that must operate efficiently. Unexpected failures cause downtime, safety risks, and high costs. IoT sensors installed on machines can generate real-time vibration, temperature, pressure, and performance metrics. Analytics on this data allows for predictive maintenance, reducing failures and optimizing maintenance schedules.

Objective

  • Analyze real-time and historical IoT sensor data to predict equipment failures before they occur.

    Enable predictive maintenance schedules, reducing unplanned downtime and maintenance costs.

    Leverage Google Cloud Dataflow for scalable streaming data processing and BigQuery ML for building predictive models.

    Provide actionable insights and dashboards for maintenance teams.

Project Description

  • This project implements a predictive maintenance system on Google Cloud:

    Data Ingestion: Collect real-time IoT sensor data from machines and historical maintenance logs.

    Data Processing: Use Dataflow to clean, aggregate, and transform streaming sensor data for analytics.

    Feature Engineering & Model Training: Store preprocessed data in BigQuery and use BigQuery ML to train predictive models (e.g., logistic regression, time-series forecasting, anomaly detection).

    Prediction & Alerting: Use trained models to predict potential failures and generate maintenance alerts.

    Monitoring & Optimization: Continuously monitor sensor data and model accuracy; retrain models with new data.

Key Technologies & Google Cloud Platform Services

  • GCP Service Purpose
    Cloud IoT Core Securely connects and manages IoT sensors on industrial equipment; collects telemetry.
    Pub/Sub Streams real-time sensor data from devices to processing pipelines.
    Dataflow Processes and transforms streaming and batch sensor data for analytics and modeling.
    BigQuery Stores structured historical and processed sensor data; supports large-scale queries and analytics.
    BigQuery ML Builds predictive ML models directly on sensor and operational data for failure prediction.
    Cloud Functions Event-driven automation for triggering alerts or maintenance notifications when models predict failures.
    Cloud Storage Stores raw sensor data, logs, and processed datasets for backup and analysis.
    Cloud Monitoring / Logging Tracks system health, sensor status, data flow performance, and model accuracy.
    Looker / Data Studio Visualizes predictive insights, failure probabilities, and maintenance schedules for operational teams.
    Cloud Key Management Service (KMS) Encrypts sensitive operational and sensor data for security and compliance.