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Explainable AI for Bearing Fault Prognosis Using Deep Learning Techniques.
Deva Chaitanya Sanakkayala1, Vijayakumar Varadarajan2,3, Namya Kumar1
1Symbiosis Institute of Technology, Symbiosis International (Deemed) University, Pune 412115, India.
Micromachines
|September 23, 2022
Summary
This study introduces a new deep learning method for predicting bearing failures in rotary machines. The approach accurately identifies defects and degradation levels, improving machine health monitoring and maintenance scheduling.
Area of Science:
- Mechanical Engineering
- Artificial Intelligence
- Signal Processing
Background:
- Bearing failures are critical in rotary machine health monitoring, impacting operational efficiency and maintenance planning.
- Automated detection of bearing defects using vibration signals offers a promising alternative to manual inspection.
- Deep learning techniques have shown significant potential in advancing automatic defect identification.
Purpose of the Study:
- To propose a novel approach for identifying bearing defects and their degradation levels under variable shaft speeds.
- To enhance machine health monitoring systems with accurate and robust fault prediction capabilities.
- To integrate explainable AI for better understanding of the deep learning model's decision-making process.
Main Methods:
- Vibration signals are pre-processed using the Short-Time Fourier Transform (STFT) to generate spectrograms.
- A Convolutional Neural Network (CNN) model, specifically VGG16, is employed for feature extraction and health status classification.
- Remaining Useful Life (RUL) prediction is performed using regression techniques.
- Explainable AI (LIME) is utilized to interpret the CNN's output and identify critical image regions.
Main Results:
- The proposed method demonstrates high accuracy and robustness in detecting bearing faults and quantifying degradation.
- Spectrogram representation combined with VGG16 CNN effectively captures bearing defect characteristics.
- The integration of RUL prediction provides valuable insights for proactive maintenance scheduling.
- LIME successfully visualizes the regions within spectrograms that are most influential for fault classification.
Conclusions:
- The developed deep learning approach offers a reliable solution for automated bearing fault diagnosis and prognosis.
- This method contributes to improved machine health monitoring by enabling early detection and accurate assessment of bearing conditions.
- The study highlights the effectiveness of combining STFT, VGG16 CNN, regression, and LIME for comprehensive bearing health analysis.
