When mechanical vibration occurs in a three-phase asynchronous motor, the poor electrical contact points in the main circuit will generate a series of fault arcs under the influence of vibration, which will compromise circuit safety and potentially lead to electrical fires. The vibration condition complicates the fault arc signal, so this paper proposes a highly real-time series fault arc detection method under vibration conditions. First, experimental current data is dynamically preserved by constructing a sliding memory matrix. Secondly, the texture features of the sliding memory matrix are extracted using orthogonality direction local ternary pattern (OD-LTP). Finally, the amplitude of the grayscale distribution histogram of the statistical OD-LTP images is taken as the feature vector. A vibrating series fault arc detection model is established using support vector machine (SVM) optimized by sand cat swarm optimization (SCSO). By comparing different matrix parameters, the proposed method achieves an accuracy of 99.2%. Through a comparative analysis of different feature extraction methods under various working conditions, it is shown that the proposed method is not only suitable for industrial motor inverter systems under different working conditions, but also exhibits higher real-time performance compared to other feature extraction methods.