Elevator Operation Health Diagnosis using Vibration Region Segmentation Algorithm via Internet

Abstract

The safety of elevator operation is an indispensable issue in the elevator industry. The most important factor affecting the elevator performance is car vibration. For this reason, vibration analysis is considered an important topic for elevator maintenance as it can be used to detect potential problems before breakdown. Currently, vibration measurement is typically performed using vibration analyzers operated by personnel, resulting in a time-consuming process and experience-dependent interpretation. While there are some machine learning algorithms that are used to diagnose elevator condition, their computational complexity still makes it difficult to translate into a real-world application. Therefore, in this study, the elevator condition diagnosis model is developed using a simple vibration region segmentation method. Based on the elevator operation characteristics, the cut-off point is determined by the abrupt acceleration variation condition to define the acceleration segment region. With the binarization process, the digital array is used at each time of acceleration variation to evaluate the state of elevator operation. Normally, the elevator operation can be divided into five segments, e.g., start up, acceleration, steady state, deceleration, and stop. The rule for determining the critical point for segmentation is thus formulated based on an abrupt acceleration change. If the number of segmented areas exceeds five, it can be considered as an abnormal case. To develop the system, the elevator vibration data is collected by a 3D accelerometer and then processed in the PC using the proposed algorithm. The results are then transferred to the cloud for online monitoring. The experimental results show that the proposed model is quite simple but effective for elevator diagnosis and maintenance.