Abstract
In smart buildings, elevator faults may affect the traveling efficiency and the safety of passengers. It is important to find a quick and accurate method for fault diagnosis to minimize disruption time. Most existing fault diagnosis methods are for gear systems, which ignore the characteristics of elevators and need adaptations before applying them to the elevators. This study presents a novel multidomain feature extraction method for elevator fault diagnosis. It calculates the time domain features and wavelet packet energy and then extracts the elevator frequency features. These multidomain features are utilized as the input of a support vector machine to diagnose elevator faults. Subsequently, SHapley Additive exPlanations is used to select features to remove redundant features without reducing diagnosis accuracy. Comparative experiments show that multidomain feature extraction based on triaxial data improves diagnosis accuracy. Additionally, the elevator frequency feature extraction method further enhances diagnosis accuracy. In addition, experiments demonstrate that SHapley Additive exPlanations feature selection can be effectively applied to remove redundant features in different fault diagnoses, which improves the training efficiency and reduces the risk of overfitting.
Practical applications
In this paper, we present a practical solution for elevator fault diagnosis in intelligent buildings. By the proposed method, maintainers can find elevator faults based on objective evidence rather than intuitive feeling. We utilize a phone instead of the vulnerable and expensive equipment to collect and analyze signals, which makes the cost for this solution inexpensive and propagable. Thus, this solution can be applied to most practical elevator maintenance to reduce the elevator downtime.
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