2020
DOI: 10.1016/j.procir.2020.04.126
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Data-driven Models for Fault Classification and Prediction of Industrial Robots

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Cited by 11 publications
(6 citation statements)
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References 15 publications
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“…Multiple supervised models such as a support vector machine, neural networks, gaussian processes and random forests were combined with different dimensionality reduction methods based on data from acceleration sensors attached to the gear caps for gear fault classification. The SVM and GP showed the best performance with accuracy values over 91 percent [14].…”
Section: Industrial Robot Condition Monitoringmentioning
confidence: 99%
“…Multiple supervised models such as a support vector machine, neural networks, gaussian processes and random forests were combined with different dimensionality reduction methods based on data from acceleration sensors attached to the gear caps for gear fault classification. The SVM and GP showed the best performance with accuracy values over 91 percent [14].…”
Section: Industrial Robot Condition Monitoringmentioning
confidence: 99%
“…1. Mathematical methods (fast and short-time Fourier transform, continuous wavelet transforms) [7,8]; 2. Modeling methods (fuzzy logic, machine learning, and other artificial intelligence methods) [8,9]; 3.…”
Section: Cartesian Robot Descriptionmentioning
confidence: 99%
“…Therefore, for diagnostics, each of the methods presented has its own scope of application and is used to achieve various goals [1,6,7]. The mathematical methods are a powerful tool for evaluating and analyzing data, thereby identifying anomalies in the behavior of the mechanism.…”
Section: Introductionmentioning
confidence: 99%
“…The changes of the RMS-HI and characteristic frequencies for functional and broken strain gears of industrial robots were investigated in [10]. The classification and regression performance of different data-driven models based on frequency-domain data and principal component analysis for dimensionality reduction was evaluated in [11].…”
Section: Vibration-based Robot Condition Monitoringmentioning
confidence: 99%