GRADA: Gradient regularized adversarial domain adaptation for remaining useful lifetime estimation

In industrial applications, variations in system configurations, degradation modes, and the lack of labeled, run-to-failure data contribute to poor generalization by a domain-specific network on data from a different domain, although for a similar task. Adversarial domain adaptation attempts to mitigate this by introducing a domain classifier that discriminates between the latent features of source and target domain encoders, thereby encouraging the latter to learn domain-invariant representations. However, the discriminator’s susceptibility to oscillatory and exploding gradients negates the convergence of the target network. We present a novel Gradient Regularized Adversarial Domain Adaptation (GRADA) framework that utilizes a parameter-aware Adaptive Gradient Shrinkage (AGS) operator to regularize the discriminator. The discriminator parameters and gradients are used to develop a criterion matrix and a subsequent adaptive threshold, through which soft-thresholding is performed to suppress noisy gradients and preserve informative ones along their actual descent paths. Theoretical proofs validate the non-explosive and stabilizing properties of the AGS operator. We also incorporate AGS into an existing contrastive adversarial framework to test its use as a regularizer irrespective of the underlying framework. Our experiments focus on the remaining useful life (RUL) estimation task using the C-MAPSS dataset, as it provides suitable cross-domain scenarios with variable operational settings and fault modes.