Welding defect detection in the manufacturing of hot water tanks is still often performed by human visual inspection or with the help of classical non-destructive tests such as liquid penetrant. These quality inspection methods can be time-consuming and have an important error rate. As a potential alternative, machine learning algorithms can be employed to automate fault detection in this process. In this paper, we propose an approach for the detection of welding faults by identifying abnormal subsequences of the welding voltage signal. The novelty of our approach is in employing the one-class SVM with distance substitution kernels, which, unlike previous works employing the one-class SVM, allows us to work directly with the raw subsequences. This permits easier generalization of the detection across different types of signals and achieves higher detection accuracy. The results show that the approach is both accurate and fast compared to existing approaches, which makes it more suitable for real-time welding monitoring. We further propose an approach for the automatic diagnosis of welding defects using the random convolutional kernel transform.