An elevator door anomaly detection method based on improved deep multi-sphere support vector data description

Abstract

Various types of elevator door faults and difficulties in fault data acquisition make it difficult to use supervised learning methods for fault diagnosis. This paper proposes a semi-supervised anomaly detection method based on improved deep multi-sphere support vector data description. Multiple distinguishing hyper-spheres, characterized by minimum volume, are established on the foundation of normal data by this method. These hyper-spheres represent the multi-modal distribution exhibited by the normal data. In addition, the method fuses multi-sensor source data such as tri-axial acceleration, dual-axial tilt angle, and introduces the structure of InceptionTime to realize the fusion of multivariate data and feature extraction in multiple resolutions. Experiments verify the feasibility of the method with an overall AUC of 96.50%, and comparative experiments demonstrate the superior detection performance. This contributes a novel, accurate, and more appropriate method to the elevator door anomaly detection.