Zero Shot Industrial Fault Diagnosis

Zero-shot industrial fault diagnosis aims to identify previously unseen equipment malfunctions without requiring training data for each specific fault type. Current research focuses on developing models that can generalize across diverse operating conditions and fault characteristics, employing techniques like graph neural networks, generative adversarial networks, and broad learning systems to achieve this. These advancements are crucial for improving industrial safety and efficiency by enabling proactive maintenance and reducing downtime, particularly in scenarios with limited labeled data or rapidly evolving equipment. The ultimate goal is to create robust and adaptable diagnostic systems capable of handling a wide range of unseen faults in real-world industrial settings.