Fault Diagnosis of Elevator Doors Using Control State Information

In this study, an integrated framework involving state classification, preprocessing, and classification is proposed for the fault diagnosis of elevator doors using control state information. During state classification, the door state is classified as operational or non-operational; moreover, based on the state information of the control board of an elevator door, the corresponding operational conditions are classified as opening or closing states. In the preprocessing phase, data processing and interpolation and feature manipulation are performed. Data are interpolated to synchronize each measurement with respect to the reference time; then, the state information is manipulated to create distinct features. In the classification phase, the operational states are classified, and nonlinear coordinate transformation is executed to transfer several features into a new nonlinear hyperspace by using an autoencoder. Two manifold features are extracted from the latent layer of the autoencoder; these become the principal axes for state classification using a support vector machine, indicating that three states are possible: normal with and without completion of a full stroke and abnormal stroke. The effectiveness of the proposed method was verified by performing field measurements on a control board during the operation of an elevator. Specifically, the autoencoder in the integrated framework effectively transformed an original space into a highly nonlinear yet efficient manifold, where classification was easy for different operational states. The proposed framework can be effective for real-world detection of elevator door faults because it exclusively uses state information measured from control boards.