Sensitivity enhanced method for fault detection and prediction of elevator doors using a margin maximized hyperspace

Abstract

This paper proposes a novel fault classification and prediction method by addressing a margin maximized hyperspace (MMH) to solve the problem, absent of any label at a highly imbalanced dataset, which is a frequent but challenging problem in real-world industries. The proposed method features three characteristics. First, knowledge-based feature manipulation is conducted using reference and feedback physical properties and the manipulated features are used for training the proposed neural network because the features contain rich information for classifying and predicting faults of the system of interest. Second, VAE transforms high-dimensional input features into a low-dimensional feature space. This nonlinear space transformation reduces the complexity of the classification securing high accuracy and robustness of fault classification in the MMH. Third, the acquired MMH through VAE with Bayesian optimization statistically allocates two extremes of major (normal) and minor (faulty) clusters at the origin and unity at the feature space, indicating that the sensitivity of fault prediction is maximized. The method would be highly effective in that the model only focuses on separating major and minor clusters deciding each health condition but ignores minor differences within the clusters which confuse users. The effect of the method is demonstrated with field measurements of an elevator door stroke dataset comprising normal, degradation, and faulty states in open and close strokes. The systematic analysis shows that these characteristics contribute to improving accuracy and robustness for fault classification. Specifically, knowledge-based feature manipulation improves accuracy, and VAE enhances sensitivity in separating each cluster and locational constancy. Moreover, the MMH is effective in predicting potential faults without any label for a highly imbalanced dataset. The proposed method provides the remaining useful lifetime (RUL) using distances from normal and faulty clusters at the MMH, which enables quantitatively providing RUL of the system without any definition of RUL. Considering that many systems deployed on fields lack information for fault life or residual useful life, the proposed method would be practical and effective for real-world applications.