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FPGA-Microprocessor Based Sensor for Faults Detection in Induction Motors Using Time-Frequency and Machine Learning

Roque Alfredo Osornio-Rios1, Isaias Cueva-Perez1, Alvaro Ivan Alvarado-Hernandez1

  • 1Cuerpo Académico (CA) Mecatrónica, Facultad de Ingeniería, Campus San Juan del Río, Universidad Autónoma de Querétaro, Av. Río Moctezuma 249, San Juan del Río 76807, Querétaro, Mexico.

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Summary
This summary is machine-generated.

This study presents a novel non-invasive method for detecting multiple induction motor (IM) faults. The system achieves nearly 99% accuracy in identifying healthy states and various fault conditions using current signals and thermal imaging.

Keywords:
FPGA sensorinduction motorsmachine learningthermographic imagestime domaintime-frequency

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Area of Science:

  • Electrical Engineering
  • Industrial Automation
  • Machine Condition Monitoring

Background:

  • Induction motors (IM) are critical industrial components, but susceptible to faults from environmental or operational changes.
  • Current fault detection trends emphasize non-invasive techniques analyzing multiple signals for comprehensive diagnostics.
  • Existing methods may lack the efficiency or accuracy needed for real-time industrial applications.

Purpose of the Study:

  • To develop and validate a highly accurate, non-invasive system for detecting multiple induction motor faults.
  • To integrate electrical current analysis with infrared thermography for enhanced fault diagnosis.
  • To implement the diagnostic system on a hardware platform for efficient real-time processing.

Main Methods:

  • Processing electric current signals using the Short-Time Fourier Transform (STFT).
  • Calculating mean and standard deviation from infrared thermograms as key indicators.
  • Combining STFT and thermal data using Support Vector Machine (SVM) and k-nearest-neighbor (KNN) classifiers.
  • Utilizing a Xilinx PYNQ Z2 board with FPGA and microprocessor for hardware acceleration.

Main Results:

  • The proposed system effectively classifies healthy (HLT) states and various faults: misalignment (MAMT), unbalance (UNB), damaged bearing (BDF), and broken rotor bar (BRB).
  • Achieved classification accuracy close to 99% for all tested conditions.
  • Demonstrated the efficacy of combining electrical and thermal data for robust fault detection.

Conclusions:

  • The integrated approach using STFT of current signals and thermal imaging provides a highly accurate and effective method for induction motor fault diagnosis.
  • Hardware implementation on an FPGA-based platform enables efficient real-time monitoring and diagnostics.
  • This non-invasive technique offers a significant advancement in industrial machine condition monitoring and predictive maintenance.