CLDDGAN for CWT Generation and Fault Classification Using LiteFormer2D

This work focuses on devising a Conditional Latent Denoising Diffusion GAN for generating CWT-based time-frequency images and improving LiteFormer, an attention-free, transformer-inspired classifier for fault classification on augmented datasets.

Work in Progress

Project Overview

This research introduces a novel approach that combines conditional latent denoising diffusion models with Generative Adversarial Networks to generate high-quality Continuous Wavelet Transform (CWT) based time-frequency representations for industrial fault diagnosis.

Key Components

• Conditional Latent Denoising Diffusion GAN: A hybrid architecture that leverages the stability of diffusion models with the adversarial training of GANs for controlled generation of CWT-based time-frequency images.

• Enhanced LiteFormer: An improved version of the attention-free, transformer-inspired classifier designed specifically for fault classification tasks on augmented datasets, providing efficient processing without traditional attention mechanisms.

Technical Innovation

The project addresses the challenge of limited fault data in industrial systems by generating realistic CWT-based time-frequency representations that capture both temporal and frequency domain characteristics of machinery signals. The conditional nature of the generation process allows for targeted synthesis of specific fault patterns.

Current Development Status

• Architecture design and implementation
• Initial experiments with CWT generation
• LiteFormer improvements and optimization
• Performance evaluation on industrial datasets

Expected Contributions

• Novel conditional diffusion-GAN hybrid for time-frequency image generation
• Improved attention-free transformer architecture for fault classification
• Enhanced data augmentation techniques for industrial fault diagnosis
• Comprehensive evaluation on real-world industrial datasets