What is it about?

Position signal faces several weak oscillations due to mechanical flaw and faults occurred in the systems. These oscillations can be identified by the encoders that determine the performance and health condition of the machine. Nevertheless, also the concerned oscillation, rotary encoder signal also includes some measurement noise and a significant trend. These trends are typically of several orders, greater in activities than the involved amplitude oscillations, making it tough to detect the small oscillations except deformation of the signal. In addition, the oscillations can be problematic, and magnitude adjusted in unstable conditions. Singular spectrum analysis (SSA) is proposed to overcome this issue. A numerical emulation is demonstrated to show the efficiency of the approach. It indicates that SSA outperforms ensemble empirical mode decomposition (EEMD), empirical mode decomposition (EMD), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) in ability and accuracy. Moreover, during the movement of the robotic arm, encoder signals from the robot are analyzed to determine the sources of oscillations in joints. The suggested technique is proven to be reliable and feasible for an industrial robot.

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Why is it important?

In this study, the industrial robot rotary encoder signals are examined to evaluate the origins and magnitude of the oscillations during the robotic arm motion. The reconstruction of both residual and oscillations for joint 1 and 2 is obtained from the movement of the payload and descends in four states after the activity of the robotic arm.


Singular spectrum analysis is proposed to use rotary encoder signals in this article to obtain weak position oscillations. The suggested SSA method decomposes the rotary encoder signal into trend, noise, and oscillations with both stage and magnitude deformation of signals

PhD Riyadh Nazar Ali ALGBURI

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This page is a summary of: Detecting feeble position oscillations from rotary encoder signal in an industrial robot via singular spectrum analysis, IET Science Measurement & Technology, December 2019, the Institution of Engineering and Technology (the IET), DOI: 10.1049/iet-smt.2019.0172.
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