Arc welding processes are vital for continuous fabrication but are susceptible to disturbances that cause defects and compromise weld quality. Real-time monitoring is therefore essential, yet remains challenging due to complex visual patterns and the nonlinear, time-varying nature of welding dynamics. While deep learning offers potential, its reliance on large, labeled datasets, and in particular, on process- and application-specific tuning, limits industrial scalability. We question whether there exists a common approach to characterize major arc processes across different applications. If so, by observing and monitoring characteristic variables as process states under a unified framework, scalability can be greatly improved. This paper presents a robust monitoring framework generalizable across arc welding processes. It integrates deep latent representation learning to extract compact features from weld pool views in an unsupervised manner and employs Bayesian filtering to enhance robustness against sensory disturbances that are both sustained and fluctuating in arc welding, such as arc radiation and specular reflection from the weld pool surface. To this end, we employ a Dynamic Variational Autoencoder (DVAE), composed of a Convolutional Neural Network (CNN)-based encoder-decoder and a Long Short-Term Memory (LSTM)-based transition model, to jointly learn compact latent representations of weld pool images and their evolution under control inputs. This setup fuses instantaneous visual representations with process dynamics modeling, enabling compact latent features that satisfy both objectives. To achieve robust real-time inference, a specialized Particle Filter (PF) is introduced to jointly propagate the latent state and the hidden state of the LSTM transition model, preserving both current and historical process information while suppressing sensor disturbances such as arc rotation and specular reflection. This design is well suited to the relatively slow and inertial dynamics of arc welding, allowing the PF to effectively fuse model-based predictions with real-time observations. The proposed framework is validated on both GTAW and GMAW processes without process-specific customization, demonstrating its generalizability and robustness.
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