Visualization of gear-motor shaft whirling feature based on time-series analysis for rotary machine component condition monitoring

Abstract

A technique for visualization of a gear-motor shaft’s whirling feature is proposed based on time-series analysis for rotary machine component condition monitoring. It is necessary to develop many technological elements, including machine components, the Internet of Things (IoT), sensing, signal processing and modeling for machine component condition monitoring. When a machine component is connected to another device, the machine component’s features change because of the connection. Specifically, this work considers the case of a machine component where the shaft around the axis connecting the component to another device does not form a circular orbit. It is assumed that the shaft does not have a circular orbit and it is thus necessary to visualize the shaft using a signal processing technique based on this assumption. In general methods, however, because a constant speed and circular orbit are assumed, some errors occur because of the noncircular orbit. In this paper, we consider visualization using a signal processing technique that focuses on the rotational axis, particularly for connections between rotary machine components for condition monitoring. In the proposed method, a time waveform is converted into polar coordinates and expressed in terms of its amplitude and angular direction. By calculating the density distribution for each angle, the features are confirmed even if the shaft orbit does not become a circle. Furthermore, it aids in judging whether the feature change has followed a machine component condition change in the trajectory. Measurement data were obtained through verification experiments. It is confirmed that the density distribution’s relative standard deviation is less than approximately 0.05 and that the orbit is constant under normal conditions. From the experimental results, it is confirmed that the proposed signal processing method is thus effective for machine component condition monitoring.