Semi-supervised convolutional generative adversarial networks for dynamic fault classification with manifold regularization

Data-driven fault classification identifies and categorizes faults or anomalies in industrial systems, to ensure efficient and safe operations. This paper addresses the challenges of semi-supervised learning for dynamic fault classification in industrial processes. The key problems involve utilizing three unique characteristics of typical industrial process dataset: 1) the presence of unlabeled samples due to high labeling costs and required expert knowledge, 2) the local manifold structure arising from strong variable coupling, and 3) time correlation inherent in time-series sensing data of dynamic processes. To tackle these challenges, a semi-supervised fault classification model, SSDCGAN, is proposed, based on deep convolutional Generative Adversarial Networks. The model captures dynamic temporal information through a moving window technique and leverages manifold regularization to maintain classifier consistency along the manifold's tangent direction. Evaluations on the Tennessee Eastman (TE) benchmark demonstrate that SSDCGAN enhances fault classification accuracy, outperforming current methods.