Sleeping stage classification based on joint quaternion valued singular spectrum analysis and ensemble empirical mode decomposition

2022 ◽  
Vol 71 ◽  
pp. 103086
Author(s):  
Zuo Huang ◽  
Bingo Wing-Kuen Ling
Keyword(s):  
Empirical Mode Decomposition ◽  
Spectrum Analysis ◽  
Singular Spectrum Analysis ◽  
Ensemble Empirical Mode Decomposition ◽  
Singular Spectrum ◽  
Mode Decomposition ◽  
Stage Classification

scholarly journals Health Assessment and Fault Detection System for an Industrial Robot Using the Rotary Encoder Signal

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2816 ◽  
Author(s):  
Riyadh Nazar Ali Algburi ◽  
Hongli Gao
Keyword(s):  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Hilbert Transform ◽  
Spectrum Analysis ◽  
Industrial Robot ◽  
Singular Spectrum Analysis ◽  
Energy Performance ◽  
Industrial Robots ◽  
Singular Spectrum ◽  
Mode Decomposition

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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