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

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.