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Predictive Maintenance with Sensor Data Analytics on a Raspberry Pi-Based Experimental Platform.

Shang-Yi Chuang1, Nilima Sahoo2, Hung-Wei Lin3

  • 1Department of Electrical Engineering, Chang Gung University, Taoyuan City 333, Taiwan. m0421011@cgu.edu.tw.

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|September 12, 2019
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Summary
This summary is machine-generated.

This study introduces a predictive maintenance system using machine learning and sensor data. The developed mechanism minimizes equipment downtime and reduces maintenance costs by enabling timely interventions.

Keywords:
PICRaspberry Pidata analysisenvironment sensingpredictive maintenance

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Area of Science:

  • Engineering
  • Computer Science
  • Data Science

Background:

  • Predictive maintenance is crucial for minimizing unexpected equipment downtime and operational costs.
  • Traditional maintenance strategies often lead to inefficiencies and premature equipment failure.
  • Advancements in sensor technology and machine learning offer new opportunities for proactive equipment management.

Purpose of the Study:

  • To develop and evaluate a predictive maintenance mechanism utilizing a custom test platform and machine learning.
  • To enhance equipment lifecycle management through data-driven maintenance scheduling.
  • To improve operational efficiency and reduce unforeseen losses in industrial settings.

Main Methods:

  • Implementation of a predictive maintenance system with a test platform and data analysis.
  • Utilizing Raspberry Pi for sensor data transmission via Transmission Control Protocol/Internet Protocol (TCP/IP).
  • Employing programmable interface controllers for environmental sensing and time-series data storage, analyzed using statistical software for modeling and prediction.

Main Results:

  • The system enables timely maintenance decisions through data preprocessing, modeling, and prediction.
  • Multivariate analysis provides comprehensive insights into equipment status and operational conditions.
  • The developed modules effectively prevent unpredictable losses and enhance service quality.

Conclusions:

  • The developed predictive maintenance mechanism successfully integrates sensor data and machine learning for proactive equipment management.
  • The system offers significant benefits in reducing downtime, lowering costs, and extending equipment lifespan.
  • This approach provides a robust solution for improving industrial operational efficiency and service quality.