Parallel Machine Learning Algorithm Using Fine-Grained-Mode Spark on a Mesos Big Data Cloud Computing Software Framework for Mobile Robotic Intelligent Fault Recognition

An accurate and efficient intelligent fault diagnosis of mobile robotic roller bearings can significantly enhance the reliability and safety of mechanical systems. To improve the efficiency of intelligent fault classification of mobile robotic roller bearings, this paper proposes a parallel machine...

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Vydané v:	IEEE access Ročník 8; s. 131885 - 131900
Hlavný autor:	Xian, Guangming
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Acceleration Accuracy Algorithms Belief networks Big Data big data Spark Classification Cloud computing cloud computing software framework empirical mode decomposition Fault detection Fault diagnosis Feature extraction Frequencies intelligent fault recognition Machine learning Machine learning algorithms Mechanical systems mesos cluster manager mobile robotic roller bearing Neural networks Nodes parallel deep belief network Parallel machine learning algorithm Parallel processing parallel support vector machine Radial basis function Robotics Robots Roller bearings Rolling bearings Segmentation Software Sparks Support vector machines Vibration
ISSN:	2169-3536, 2169-3536
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