Gearbox Fault Diagnosis Using Image Classification and AI
ImageClassification
Developed a method to convert vibration and acoustic time series data from gearboxes into images for fault diagnosis. Used image classification via deep convolutional neural networks (CNNs) to label and categorize fault types in spur and bevel gears. Collected and annotated raw signal data, converting them into labeled datasets for training AI models.
• Preprocessed acoustic and vibration signals to generate image representations.
• Designed and implemented a Multi-Input CNN to improve accuracy in labeling gear faults.
• Labeled image datasets representing different gearbox failure modes.
• Evaluated model performance based on labeled data for research publications.
Developed a method to convert vibration and acoustic time series data from gearboxes into images for fault diagnosis. Used image classification via deep convolutional neural networks (CNNs) to label and categorize fault types in spur and bevel gears. Collected and annotated raw signal data, converting them into labeled datasets for training AI models. • Preprocessed acoustic and vibration signals to generate image representations. • Designed and implemented a Multi-Input CNN to improve accuracy in labeling gear faults. • Labeled image datasets representing different gearbox failure modes. • Evaluated model performance based on labeled data for research publications.
2021 - 2022