While diffusion Multimodal Large Language Models (dMLLMs) have recently achieved remarkable strides in multimodal generation, the development of interpretability mechanisms has lagged behind their architectural evolution.Unlike traditional autoregressive models that produce sequential activations, diffusion-based architectures generate tokens via parallel denoising, resulting in smooth, distributed activation patterns across the entire sequence.Consequently, existing Class Activation Mapping (CAM) methods, which are tailored for local, sequential dependencies, are ill-suited for interpreting these non-autoregressive behaviors.To bridge this gap, we propose Diffusion-CAM, the first interpretability method specifically tailored for dMLLMs.We derive raw activation maps by differentiably probing intermediate representations in the transformer backbone, accordingly capturing both latent features and their class-specific gradients.To address the inherent stochasticity of these raw signals, we incorporate four key modules to resolve spatial ambiguity and mitigate intra-image confounders and redundant token correlations.Extensive experiments demonstrate that Diffusion-CAM significantly outperforms SoTA methods in both localization accuracy and visual fidelity, establishing a new standard for understanding the parallel generation process of diffusion multimodal systems.Code is available at https:// github.com/ZzzzzZhhmm/Diffusion-CAM

This article proposes a generalized neural continual learning-based cybertwin (GNC) modeling framework to realize developing one wind turbine (WT) cybertwin serving multiple modeling tasks in the wind farm operations and maintenance (O&M). A generalized WT cybertwin modeling problem, which considers modeling one cybertwin for multiple tasks without additional computational burden, is studied for the first time. Fully connected neural networks are adopted as the backbone for developing the GNC model. The online elastic weight consolidation method is incorporated to mitigate the catastrophic forgetting phenomenon among different modeling tasks. Computational experiments are conducted to validate the effectiveness of the proposed GNC framework based on the supervisory control and data acquisition data. Modeling tasks in three important problems of the wind farm O&M, the WT gearbox failure detection, WT blade breakage detection, and wind power prediction, are considered in the experiment. Compared with other benchmarking models, such as the multiple neural cybertwins, neural cybertwin, and regularized neural cybertwin, the proposed GNC can achieve high accuracies on both new tasks and existing tasks, which further verifies the WT cybertwin generalization via the proposed GNC.

The wind turbine blade breakage is a catastrophic failure to a wind farm. Its earlier detection is critical to prevent the unscheduled downtime and loss of whole assets. This article presents a conditional convolutional autoencoder-based monitoring method, which is of twofold, for identifying wind turbine blade breakages. First, a novel conditional convolutional autoencoder taking a multivariate set of data as input is developed to derive reconstruction errors, which reflect changes of system dynamics caused by impending blade breakages. Next, a statistical process control principle is applied to develop boundaries for triggering blade breakage alarms based on reconstruction errors. The effectiveness of the conditional convolutional autoencoder-based method is validated with datasets collected by supervisory control and data acquisition systems installed in multiple commercial wind farms. We also demonstrate advantages of the conditional convolutional autoencoder-based monitoring method by benchmarking against the classical autoencoder and conditional autoencoder-based monitoring methods.