Industrial non-intrusive load monitoring (NILM) presents unique challenges compared to residential settings, including complex three-phase power systems, diverse load types, and high measurement noise. This paper proposes applying the symmetrical component transform (SCT) — a classical power systems technique — as a feature extraction step for industrial appliance classification in NILM. The SCT decomposes three-phase current and voltage signals into positive, negative, and zero sequence components, which encode distinct physical properties of each load type. We integrate SCT-derived features with a convolutional neural network classifier and evaluate the approach on the LILACD industrial dataset. Experimental results demonstrate that SCT features significantly improve classification accuracy over raw signal approaches, particularly for distinguishing motor-driven loads and non-linear power electronics. Our work bridges classical power engineering knowledge with modern deep learning for more effective industrial NILM.