Phase-based time domain averaging (PTDA) for fault detection of a gearbox in an industrial robot using vibration signals. (April 2020)

• Record Type: Journal Article
• Authors: Kim, Yunhan; Park, Jungho; Na, Kyumin; Yuan, Hao; Youn, Byeng D.; Kang, Chang-soon
• Is Part Of: Mechanical systems and signal processing. Volume 138(2020)
• Publication Date: 2020-04
• Subjects: Industrial robot -- Gearbox -- Fault detection -- Vibration signal -- Time domain averaging (TDA); Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.106544
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: We propose a new phase-based time domain averaging (PTDA) method. A systematic approach is proposed to detect fault of a gearbox in an industrial robot. The proposed method is demonstrated by the experimental data from a six-degree-of-freedom (6-DOF) industrial robot test-bed. The proposed method shows better performance compared to the previous method. Abstract: This paper proposes a fault detection method that uses vibration signals in the gearboxes of industrial robots. The vibration signals from gearboxes consist of both deterministic signals and residual signals; fault-related signals usually exist in the residual signals. Previously, time domain averaging (TDA) has been studied to derive the deterministic signals. However, the performance of TDA method is limited when the signals are poorly synchronized. Therefore, we propose a new phase-based time domain averaging (PTDA) method. The proposed PTDA method can estimate deterministic signals that are more synchronized by considering the phase angle of the vibration signals. Then, the residual signals can be calculated by subtracting the estimated deterministic signals from the measured vibration signals using the PTDA method. We use two health features, root-mean-square (RMS) and power spectrum entropy, to quantify the fault severity in the residual signals. To demonstrate the proposed method, we use vibration signals measured from a six-degree-of-freedom (6-DOF) industrial robot test-bed under 1) a simple one-joint rotating motion, 2) a complicated arc welding motion, and 3) a spot welding motion. The results show that the proposed PTDA method can improve the performance of fault detection for gearboxes in industrial robots.