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Data-Driven Fault Detection and Diagnosis in Cooling Units Using Sensor-Based Machine Learning Classification.

Amilcar Quispe-Astorga1, Roger Jesus Coaquira-Castillo1, L Walter Utrilla Mego2

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Summary
This summary is machine-generated.

This study introduces an automated system for real-time fault detection in Precision Air Conditioning (PAC) systems. The Random Forest model achieved high accuracy, improving system efficiency and reducing energy consumption.

Keywords:
data-drivenfault detection and diagnosismachine learningsensorssystem cooling unit

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Area of Science:

  • Engineering
  • Computer Science

Background:

  • Precision Air Conditioning (PAC) systems are critical for sensitive environments but susceptible to failures.
  • System failures lead to inefficiencies, increased energy use, and reduced equipment performance.

Purpose of the Study:

  • To develop an automatic, real-time fault detection and diagnosis system for PAC units.
  • To classify system events as normal or faulty using sensor data analysis.

Main Methods:

  • Utilized data-driven machine learning models on sensor data (pressure, temperature, current, voltage).
  • Implemented and compared multiple models including SVM, DT, GB, KNN, NB, and RF.
  • Collected and preprocessed a dataset of 20,000 PAC operational samples.

Main Results:

  • The Random Forest (RF) model demonstrated superior performance with 96% accuracy in offline tests and 95.28% in real-time.
  • Other models showed varying accuracies: SVM (93%), DT (93%), GB (91%), KNN (83%), NB (77%).
  • A real-time validation test of the RF model achieved 93.49% accuracy.

Conclusions:

  • The proposed data-driven system effectively detects and diagnoses faults in PAC systems in real-time.
  • The Random Forest model is highly effective for this application, enhancing system reliability and efficiency.