Operation Condition Assessment for Elevators Based on Multidomain Generalization and Adaptation

The performance level and health condition of elevators seriously affect occupants' safety. Condition assessment plays an important role in the prognostics and health management of elevators. However, it has been found through our previous research that there is a serious domain shift problem in the process of elevator condition evaluation, which creates a severe influence on the evaluation model's generalization performance. Therefore, this article proposes a novel multidomain generalization and adaptation approach to address the domain shift problem in elevator condition assessment due to performance degradation caused by different factors. First, a multidomain generalization (DG) method introducing perturbation and gradient matching is developed to solve the dilemma of local optimality and overfitting problems in DG. Subsequently, the evaluation model that has acquired knowledge from the seen domains is split into backbone and classification head, and moved into the unseen domain for adaptation. Second, to deal with the high cost of adding data annotations and the lack of visibility of domain information, a special unsupervised multidomain adaptation (DA) method is designed to train the evaluation model on the unseen domains. Concretely, information entropy maximization, clustering pseudolabel cross entropy, and cross-domain feature alignment are employed as the total optimization objective for unsupervised adaptation of data on unseen domains. The validity and feasibility of the proposed method are verified by the elevator operation condition data obtained from the acquisition experiment. Finally, through ablation experiments and comparison experiments with state-of-the-art methods, our method has shown optimal results, while highlighting its necessity and advancement.