Data Generation via Fault Evolution Modeling for Unseen Level Fault Diagnosis

Faults in complex industrial equipment typically exhibit continuous evolutionary characteristics. However, existing diagnostic methods are generally limited to discrete level fault classification, making it challenging to cover the full spectrum of fault severities and thereby restricting the generalization capability of diagnostic models. This article proposes a novel approach that models the fault evolution process to generate fault data corresponding to unseen level faults, enabling the diagnosis of these unseen level faults. Specifically, within the framework of a temporal variational autoencoder, we employ neural controlled differential equations combined with gated recurrent units as the temporal encoder to capture the posterior distribution of the data. Simultaneously, the prior distribution in the latent space is modeled using the Koopman operator, allowing for linear modeling of the latent dynamics associated with fault evolution and uncovering the underlying patterns of system degradation. By sampling latent representations corresponding to unseen level faults, we generate synthetic fault data to train a model capable of diagnosing unseen level faults. Experimental results on a ground source heat pump system demonstrate that the proposed method can generate high-quality data for unseen level faults and exhibits strong performance in diagnosing unseen level faults.