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A two-step machine learning approach for predictive maintenance and anomaly detection in environmental sensor

Saiprasad Potharaju1, Ravi Kumar Tirandasu2, Swapnali N Tambe3

  • 1Department of CSE, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India.

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|February 21, 2025
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Summary

This study introduces a machine learning method to detect anomalies and predict sensor failures in environmental monitoring systems. The approach effectively uses unlabeled data for improved reliability and predictive maintenance.

Keywords:
Anomaly detectionEnvironmental sensor systemsIntegration of Unsupervised and Supervised learningPredictive maintenanceSupervised learningUnsupervised learning

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Area of Science:

  • Environmental Science
  • Data Science
  • Machine Learning

Background:

  • Environmental sensor systems are crucial for infrastructure and environmental quality monitoring.
  • Sensor faults and anomalies can compromise the reliability of these systems.
  • Existing methods struggle with unlabeled sensor telemetry data.

Purpose of the Study:

  • To develop a novel methodology for anomaly detection and sensor failure prediction.
  • To address the challenge of unlabeled data in environmental sensor telemetry.
  • To enhance the reliability of environmental monitoring systems through predictive maintenance.

Main Methods:

  • A hybrid approach combining unsupervised (Isolation Forest) and supervised machine learning models.
  • Unsupervised learning used to generate labels for unlabeled sensor data.
  • Supervised models (Random Forest, Neural Network, AdaBoost) trained on labeled data for anomaly prediction.

Main Results:

  • The proposed framework achieved high accuracy in anomaly detection and sensor failure prediction.
  • Random Forest: 99.93%, Neural Network (MLP Classifier): 99.05%, AdaBoost: 98.04%.
  • Demonstrated the effectiveness of using Isolation Forest for labeling unlabeled IoT sensor data.

Conclusions:

  • The methodology successfully transforms raw, unlabeled IoT sensor data into actionable insights.
  • Provides a scalable and robust real-time solution for anomaly detection and sensor fault prediction.
  • Advances intelligent infrastructure management and enhances the reliability of environmental monitoring.