Performative Drift is a special type of Concept Drift that occurs when a model's predictions influence the future instances the model will encounter. In these settings, retraining is not always feasible. In this work, we instead focus on drift understanding as a method for creating drift-resistant classifiers. To achieve this, we introduce the Generative Domain Adversarial Network (GDAN) which combines both Domain and Generative Adversarial Networks. Using GDAN, domain-invariant representations of incoming data are created and a generative network is used to reverse the effects of performative drift. Using semi-real and synthetic data generators, we empirically evaluate GDAN's ability to provide drift-resistant classification. Initial results are promising with GDAN limiting performance degradation over several timesteps. Additionally, GDAN's generative network can be used in tandem with other models to limit their performance degradation in the presence of performative drift. Lastly, we highlight the relationship between model retraining and the unpredictability of performative drift, providing deeper insights into the challenges faced when using traditional Concept Drift mitigation strategies in the performative setting.

The application of unsupervised domain adaptation (UDA)-based fault diagnosis methods has shown significant efficacy in industrial settings, facilitating the transfer of operational experience and fault signatures between different operating conditions, different units of a fleet or between simulated and real data. However, in real industrial scenarios, unknown levels and types of noise can amplify the difficulty of domain alignment, thus severely affecting the diagnostic performance of deep learning models. To address this issue, we propose an UDA method called Smart Filter-Aided Domain Adversarial Neural Network (SFDANN) for fault diagnosis in noisy industrial scenarios. The proposed methodology comprises two steps. In the first step, we develop a smart filter that dynamically enforces similarity between the source and target domain data in the time-frequency domain. This is achieved by combining a learnable wavelet packet transform network (LWPT) and a traditional wavelet packet transform module. In the second step, we input the data reconstructed by the smart filter into a domain adversarial neural network (DANN). To learn domain-invariant and discriminative features, the learnable modules of SFDANN are trained in a unified manner with three objectives: time-frequency feature proximity, domain alignment, and fault classification. We validate the effectiveness of the proposed SFDANN method based on two fault diagnosis cases: one involving fault diagnosis of bearings in noisy environments and another involving fault diagnosis of slab tracks in a train-track-bridge coupling vibration system, where the transfer task involves transferring from numerical simulations to field measurements. Results show that compared to other representative state of the art UDA methods, SFDANN exhibits superior performance and remarkable stability.