Physics-Aware Deep Learning on Multiphase Flow Problems

Lin, Zipeng

doi:10.4236/cn.2021.131001

Cited by 5 publications

(1 citation statement)

References 16 publications

(15 reference statements)

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“…where, u(t) is the current; ω is the resonant current polarity. On this basis, the voltage and current in one switching cycle are analyzed [14]. Assume that the output spectral sequence of the electrical fault feature of the robot is h i (t), the electrical fault distortion sequence of the robot is z i (t), and the beam oscillation sequence of voltage and current under steady-state conditions is the following formula:…”

Section: Data Normalizationmentioning

confidence: 99%

Robot Fault Detection Based on Big Data

Luo

2023

Journal of Control Science and Engineering

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In order to improve the reliability of robot electrical fault detection and diagnosis, the author proposes a robot electrical fault detection and diagnosis method based on deep learning. Taking the return power and active power as constraints, the electrical fault data collection of the robot is carried out. Taking the resonant inductance and resonant capacitance of the robot electrical equipment as identification parameters, we conduct electrical fault differential feature mining. The fault features are extracted according to the time-delay distribution sequence of the electrical fault data of the robot, and the electrical fault detection and diagnosis results are output by using the deep learning function. Simulation results show that the author's method has a high accuracy probability for robot electrical fault diagnosis. The author's method is on average 14.7% higher than the neural network-based method and 24.5% higher than the expert system-based method. The accuracy rate of the author's method for robot electrical fault diagnosis is high. The author’s method is 16.6% higher than the neural network-based method on average and 34.2% higher than the expert system-based method. It is proved that the robot electrical fault detection and diagnosis based on deep learning has high accuracy and short time.

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Section: Data Normalizationmentioning

confidence: 99%