Data Synchronization for Vibration and SCADA of Lifting Machinery Based on Correlation Analysis

Lifting machines such as elevators and cranes are typically equipped with both a Supervisory Control and Data Acquisition (SCADA) system and a Condition Monitoring System (CMS) to monitor operational and health status, ensuring reliable performance. The SCADA system collects operational data like speed, load, and current, while the CMS collects vibration data. Fusing data from both systems can significantly enhance condition monitoring performances. However, as the two types of data are collected separately with different time clocks, they are asynchronous. This paper proposes a correlation analysis-based method to synchronize SCADA and vibration data. First, vibration data is preprocessed to extract a time-frequency ridge, a feature proportional to the machine’s running speed. Second, the SCADA speed is interpolated and segmented to make sure that the sampling frequencies of the two types of data are matched. Third, the moving correlation coefficient between the time-frequency ridge and the processed SCADA speed is calculated. The time lag between the two datasets is identified when this coefficient is maximized. The subsequent data is synchronized by applying this lag. An experiment on a commissioned elevator validates the method’s effectiveness, showing it can successfully synchronize SCADA and vibration data, thereby ensuring time consistency for robust multi-source data fusion.