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.
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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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Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal Energies
In an industrial robot, rotary encoders have been extensively used for dynamic control and positioning. This study shows that the encoder signal, after appropriate processing, can also be efficiently utilized for the health observation of energy performance of industrial robots system. Singular spectrum analysis (SSA) and Hilbert transform (HT) is proposed in this work, for detecting weak position oscillations to estimate the instantaneous amplitudes (IA) and the instantaneous frequencies (IF) of an industrial robot based on the encoder signal. Compared with empirical mode decomposition (EMD) and HT, the singular spectrum analysis and Hilbert transform (SSAHT) outperforms empirical mode decomposition Hilbert transform (EMDHT) in terms of ability and precision to determine source noise, and it can accurately catch the weak oscillations without signal deformation in both position and speed introduced via mechanical flaws. Combined with SSA, the IA and IF of both oscillations and residual are extracted by HT. They are obtained from the robot arm movement. These features play an important role in improving the performance detecting weak oscillations and the residual, essential information to evaluate the health conditions and fault detection to serve the energy performance for the industrial robot. The efficiency of the proposed system has been verified both numerical simulation and experimental data. The outcomes prove that the proposed SSAHT can detect flaw indications and additionally, it can also identify faulty components. Thus, the study presents a promising tool for the health monitoring of an industrial robot instead of the vibration-based monitoring scheme.
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