On-line condition monitoring of servo motor drive systems by HHT in Industry 4.0

Abstract

Industry 4.0, the current trend of automation and data exchange in manufacturing technologies, includes the development of cyber-physical systems, the Internet of Things, and cloud computing, which create what has been called a ‘smart factory.’ In order to prevent digital manufacturing production lines in smart factories from shutting down, the development of self-diagnostic techniques is a significant issue in Industry 4.0. Thus, this paper presents a novel on-line condition monitoring method that is based on the measured line current of a brushless DC motor drive system. The characteristic component of line current is extracted by the time–frequency signal processing method, namely the ensemble empirical mode decomposition-based Hilbert Huang Transform (EEMD-based HHT), and then the mean value of characteristic component of current utilized for health monitoring is obtained. For the sake of increasing the reliability of the results, gauge repeatability and reproducibility is employed to evaluate the reliability of the computed mean value of the characteristic current. A healthy reference index, defined from the characteristic intrinsic mode function, is proposed for decision-making during on-line monitoring. Theoretical analysis and experiments are conducted to evaluate the effectiveness of the proposed health monitoring approach.