ZERO SHOT FAULT CLASSIFICATION FOR RARE COMPRESSOR FAILURE EVENTS USING GENERATIVE MODELS

The fact that rare failure events in industrial compressors are challenging to data-driven fault diagnose is a longstanding problem due to the fact that the conditions that result in such events are hard to reproduce and may not be
present in historical data. This paper presents a zero-shot learning method with the help of generative models to
categorize compressor faults with no real training data. The generation of synthetic fault signature is based on a cycleconsistent adversarial framework and is verified by similarity checks on the distribution level to maintain the synthetic
fault signature to be representative of realistic abnormal behavior. Such samples are created and matched with
semantic fault descriptions so that a zero-shot classifier can experience missed failure types based solely on their
characteristics. The compressor vibration data experiments demonstrate that the generative-zero-shot pipeline is better
at recognizing the infrequent faults in contrast with the traditional supervised and anomaly-based baselines and is
notably well-performing in instances where the real samples are very scarce or absent. The results indicate that
generative augmentation with zero-shot inference should be regarded as a promising future in early-stage detection of
low-frequency failures in rotating machinery and can be used to complement industrial predictive-maintenance
processes in which data scarcity is the reality.