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.
Highlights
Big data processing technologies are being rapidly developed given the increase in the amount of information being stored in recent years
The features of fault vibration signals collected from a mobile robotic roller bearing are extracted by empirical mode decomposition (EMD) [3], [4] and inputted to a parallel support vector machine (SVM) [5], [6] based on a big data cloud computing software framework for classification
This paper proposes a fault diagnosis technique for mobile robotic roller bearing faults using a parallel EMD-SVM machine learning method considering the non-stationary characteristics of the fault vibration signals
Summary
Big data processing technologies are being rapidly developed given the increase in the amount of information being stored in recent years. The features of fault vibration signals collected from a mobile robotic roller bearing are extracted by empirical mode decomposition (EMD) [3], [4] and inputted to a parallel support vector machine (SVM) [5], [6] based on a big data cloud computing software framework for classification. The proposed parallel EMD-SVM [19]–[23] machine learning method based on Spark is utilized for the fault diagnosis of robotic roller bearings. C. PERFORMANCE OF PARALLEL EMD-SVM IN A BIG DATA CLOUD COMPUTING NETWORK To compare the intelligent fault diagnosis results of different techniques, two other classifiers, namely DBN and RBFNN, were used in addition to the multi-class SVM.
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