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A Machine Learning Implementation to Predictive Maintenance and Monitoring of Industrial Compressors.

Ahmad Aminzadeh1,2, Sasan Sattarpanah Karganroudi2, Soheil Majidi2

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This study integrates machine learning and the Internet of Things (IoT) for predictive maintenance on industrial air compressors. The system accurately predicts equipment failures, enabling proactive maintenance and reducing downtime.

Keywords:
SQL databasesdata acquisitionlinear regressionmachine learningpredictive maintenance

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Area of Science:

  • Engineering
  • Computer Science
  • Data Science

Background:

  • Predictive maintenance is crucial for industrial equipment efficiency.
  • Traditional maintenance strategies are often reactive, leading to unexpected downtime.
  • Integrating advanced data analytics offers a path towards proactive maintenance.

Purpose of the Study:

  • To deploy an integrated system for predictive maintenance on an industrial air compressor.
  • To leverage machine learning and the Internet of Things (IoT) for real-time equipment monitoring.
  • To shift maintenance strategies from reactive to proactive through accurate failure prediction.

Main Methods:

  • Utilized multi-sensor data acquisition (temperature, pressure, flow rate) from an industrial air compressor.
  • Implemented a cloud-based system for data storage (SQL database) and analysis.
  • Developed and deployed a Linear Regression machine learning model for failure prediction with real-time inference.

Main Results:

  • The integrated system achieved 98% accuracy using the Mean Squared Error metric.
  • The system successfully predicted potential equipment failures based on sensor data thresholds.
  • Warning emails were automatically sent to operators upon detecting abnormal conditions.

Conclusions:

  • The implemented system demonstrates high accuracy in predicting industrial air compressor failures.
  • Integrating IoT and machine learning facilitates a transition to proactive maintenance strategies.
  • The approach offers significant potential for improving maintenance efficiency, equipment uptime, and cost savings.