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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Vydáno v:	IEEE access Ročník 8; s. 131885 - 131900
Hlavní autor:	Xian, Guangming
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Piscataway IEEE 2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	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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Abstract	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 learning algorithm using fine-grained-mode Spark on a Mesos big data cloud computing software framework. Through the segmentation of datasets and the support of a parallel framework, the parallel processing technology Spark is combined with a support vector machine (SVM), and a parallel single-SVM algorithm is realized using Scala language. In this approach, empirical mode decomposition (EMD) is applied to extract the energy of the acceleration vibration signal in different frequency bands as features. The parallel EMD-SVM approach is applied to detect faults in mobile robotic roller bearings from fault vibration signals. The experimental results show that it can accurately and effectively identify the faults, and it outperforms existing methods based on parallel deep belief network (DBN) and parallel radial basis function neural network under different training set sizes. Fault classification tests are conducted on outer-race and inner-race faults: in both cases, the proposed parallel EMD-SVM outperforms the serial EMD-SVM in terms of the classification accuracy and classification time under different test sizes. For a small number of nodes, the processing time of the proposed Spark model is less than that of Hadoop but close to that of Storm. For 17 slave nodes, the average precision, average recall, and average F1 score of Spark on Mesos in the fine-grained mode reach 97.3, 97.8, and 97.9%, respectively. The parallel EMD-SVM algorithm in the big data Spark cloud computing framework can improve the accuracy of intelligent fault classification, albeit by a small margin, with higher processing speed and learning convergence rate.
AbstractList	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 learning algorithm using fine-grained-mode Spark on a Mesos big data cloud computing software framework. Through the segmentation of datasets and the support of a parallel framework, the parallel processing technology Spark is combined with a support vector machine (SVM), and a parallel single-SVM algorithm is realized using Scala language. In this approach, empirical mode decomposition (EMD) is applied to extract the energy of the acceleration vibration signal in different frequency bands as features. The parallel EMD-SVM approach is applied to detect faults in mobile robotic roller bearings from fault vibration signals. The experimental results show that it can accurately and effectively identify the faults, and it outperforms existing methods based on parallel deep belief network (DBN) and parallel radial basis function neural network under different training set sizes. Fault classification tests are conducted on outer-race and inner-race faults: in both cases, the proposed parallel EMD-SVM outperforms the serial EMD-SVM in terms of the classification accuracy and classification time under different test sizes. For a small number of nodes, the processing time of the proposed Spark model is less than that of Hadoop but close to that of Storm. For 17 slave nodes, the average precision, average recall, and average F1 score of Spark on Mesos in the fine-grained mode reach 97.3, 97.8, and 97.9%, respectively. The parallel EMD-SVM algorithm in the big data Spark cloud computing framework can improve the accuracy of intelligent fault classification, albeit by a small margin, with higher processing speed and learning convergence rate.
Author	Xian, Guangming
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SubjectTerms	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
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Title	Parallel Machine Learning Algorithm Using Fine-Grained-Mode Spark on a Mesos Big Data Cloud Computing Software Framework for Mobile Robotic Intelligent Fault Recognition
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