Abstract
K-Means clustering algorithm is a typical unsupervised learning method, and it has been widely used in the field of fault diagnosis. However, the traditional serial K-Means clustering algorithm is difficult to efficiently and accurately perform clustering analysis on the massive running-state monitoring data of rolling bearing. Therefore, a novel fault diagnosis method of rolling bearing using Spark-based parallel ant colony optimization (ACO)-K-Means clustering algorithm is proposed. Firstly, a Spark-based three-layer wavelet packet decomposition approach is developed to efficiently preprocess the running-state monitoring data to obtain eigenvectors, which are stored in Hadoop Distributed File System (HDFS) and served as the input of ACO-K-Means clustering algorithm. Secondly, ACO-K-Means clustering algorithm suitable for rolling bearing fault diagnosis is proposed to improve the diagnosis accuracy. ACO algorithm is adopted to obtain the global optimal initial clustering centers of K-Means from all eigenvectors, and the K-Means clustering algorithm based on weighted Euclidean distance is used to perform clustering analysis on all eigenvectors to obtain a rolling bearing fault diagnosis model. Thirdly, the efficient parallelization of ACO-K-Means clustering algorithm is implemented on a Spark platform, which can make full use of the computing resources of a cluster to efficiently process large-scale rolling bearing datasets in parallel. Extensive experiments are conducted to verify the effectiveness of the proposed fault diagnosis method. Experimental results show that the proposed method can not only achieve good fault diagnosis accuracy but also provide high model training efficiency and fault diagnosis efficiency in a big data environment.
Highlights
Rolling bearing is one of the most commonly used and damaged components of rotating machinery equipment, and rolling bearing fault diagnosis is very important to ensure the normal running of rotating machinery equipment [1]
Zhang et al [20] improved the choice method of initial clustering centers of K-Means, and the results show that the fault diagnosis accuracy of rolling bearing obtained using the modified K-Means clustering algorithm is increased by 7.5% than that obtained using the traditional K-Means clustering algorithm
The parallelization of ant colony optimization (ACO)-K-Means clustering algorithm for rolling bearing fault diagnosis is implemented on a Spark platform, which can efficiently and accurately perform clustering analysis on the massive running-state monitoring data of rolling bearing
Summary
Rolling bearing is one of the most commonly used and damaged components of rotating machinery equipment, and rolling bearing fault diagnosis is very important to ensure the normal running of rotating machinery equipment [1]. L. Wan et al.: Novel Bearing Fault Diagnosis Method Using Spark-Based Parallel ACO-K-Means Clustering Algorithm suitable for complex fault diagnosis of rolling bearing. In view of the obvious advantages of Spark and K-Means clustering algorithm in industrial big data analysis, this paper proposes a novel fault diagnosis method of rolling bearing using Spark-based parallel ACO-K-Means clustering algorithm, which can effectively diagnose rolling bearing faults through rapid and accurate mining of fault information from the massive running-state monitoring data of rolling bearing. The parallelization of ACO-K-Means clustering algorithm for rolling bearing fault diagnosis is implemented on a Spark platform, which can efficiently and accurately perform clustering analysis on the massive running-state monitoring data of rolling bearing.
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