A Hidden Markov Framework for Physically Interpretable Arc Stability Dynamics in Welding Systems

TL;DR

This paper introduces a probabilistic state-space model using Hidden Markov Models to interpret arc stability in welding systems, capturing dynamic regimes with physical spectral features.

Contribution

It presents a novel HMM-based framework that models arc stability as evolving latent regimes using physically meaningful spectral descriptors.

Findings

Identified three dominant arc regimes: transient, stable, and extinction.

Spectral energy increases while entropy decreases under stable conditions.

The inferred state sequence shows high temporal coherence despite feature overlap.

Abstract

Electric arc welding (EAW) exhibits strongly non stationary and temporally evolving behavior, making reliable assessment of arc stability difficult using conventional frame based approaches. In this study, arc dynamics are modeled as a sequence of latent operational regimes within a probabilistic state-space framework. The welding current signal is transformed into a time-frequency domain using Short-Time Fourier Transform (STFT), and a set of physically meaningful spectral descriptors, including energy, entropy, and centroid, is extracted to construct the observation sequence. A Hidden Markov Model (HMM) is employed to capture temporal dependencies and estimate the evolution of arc states. The analysis reveals three dominant regimes, transient, stable, and extinction, with a clear monotonic increase in spectral energy and a corresponding decrease in entropy, indicating reduced variability under stable conditions. Despite partial overlap in the feature space, the inferred state sequence exhibits strong temporal coherence, supported by high state persistence and low transition rates. These findings highlight the limitations of static classification and emphasize the importance of temporal modeling. The proposed framework provides an interpretable and physically consistent representation of arc behavior, enabling more realistic monitoring and analysis of stability dynamics in welding processes.