ECMFGN: Extended Conditional Mapping Feature Generation Network for Severe Few-Shot Fault Diagnosis of Chillers

In severe few-shot scenarios, fault samples are extremely limited, challenging the health management of the chiller significantly. Generative adversarial network (GAN), a common data augmentation method, suffers from a feature shift issue in severe few-shot scenarios. This is because the extremely limited fault samples cannot provide sufficient distribution paradigms for GANs. Therefore, a severe few-shot fault diagnosis method for chillers based on an extended conditional mapping feature generation network (ECMFGN) is proposed to tackle this issue. In ECMFGN, the ECM module is built to provide an extended distribution paradigm for the generator. This extended distribution paradigm can adequately reflect class-specific characteristics. Then, through adversarial supervised learning, the feature similarity between the samples synthesized by the generator and the real samples is improved. Experiments demonstrate that the feature shift issue in severe few-shot scenarios is solved effectively. In addition, the characteristics of different fault modes for chillers are close. Thus, insufficient intraclass cohesion and interclass separation can lead to interclass confusion of generated samples, which directly affects diagnostic accuracy. To conquer this challenge, a smooth <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> triplet loss (TL) is designed to promote intraclass cohesion while improving the separability between classes. The experimental results indicate that the performance of the fault diagnosis model is significantly improved in the chiller dataset with similar characteristics among the fault modes, benefiting the health management and maintenance decisions of the equipment.