Margin-maximized hyperspace for fault detection and prediction: A case study with an elevator door

Abstract

This study proposes a practical fault detection and prediction method by addressing a margin-maximized hyperspace. The proposed method is effective for a highly imbalanced dataset without any supervision, which is a frequently occurring and challenging problem in real-world applications. The proposed method has three characteristics. First, knowledge-based feature manipulation is executed to provide sufficient information for a neural network. Second, a regulated variational autoencoder transforms distinct input features into a latent space, which ensures high accuracy and robustness. Third, the obtained latent space is confirmed to statistically allocate two extremes of major (normal) and minor (faulty) clusters at an origin and unity, maximizing the sensitivity to classify faults. The effectiveness of the proposed method is demonstrated through field measurements of elevator door-strokes and showed high sensitivity to separate each cluster along with locational constancy compared to other autoencoders. Therefore, the proposed method is effective for real-world applications with scarce fault measurements.