Mechanical vibration monitoring system for electrocardiogram machine based on Hilbert‐Huang transformations

Abstract

The monitoring of health and the technologies that are related to it are an exciting area of research. The paper proposes a mechanical manufacturing vibration monitoring system that is based on Hilbert-Huang transformation (HHT) feature extraction to monitor the running state of the spindle of a mechanical numerical control (NC) machine tool of an electrocardiogram (ECG) machine. Real-time monitoring of the time-frequency characteristic quantity of the spindle vibration signal for ECG signals has been made possible due to the online empirical mode decomposition (EMD) method, which is used to obtain the time-frequency characteristic quantity of the spindle vibration signal based on HHT. The experiment shows that the frequency doubling characteristic components in the timefrequency distribution are obvious in the time interval without copper rod contact, but they disappear in the time interval during which copper rods are in contact (0.3 1.1 s, 3 4s in the figure). It has been demonstrated that the system is capable of not only accurately monitoring the characteristic quantity in the frequency domain of the vibration signal produced by the NC machine tool spindle, but also of successfully implementing the monitoring of the time-frequency characteristic quantity in real time.

Vibration Signal Analysis of Complex Mechanical Systems and Early Wear Detection and Forecasting for Gears

“…While previous research has proposed myriad methods regarding vibration signal analysis and gear wear detection, these methods often exhibit limitations when addressing complex mechanical systems [15][16][17][18]. Conventional detection approaches largely rely on empirical rules and statistical techniques, possibly failing to represent the true wear status of gears accurately, thus prone to false alarms or omissions [19,20].…”