断层(地质)
计算机科学
方位(导航)
粗集
集合(抽象数据类型)
支持向量机
模式识别(心理学)
特征(语言学)
领域(数学分析)
振动
构造(python库)
特征向量
数据挖掘
时域
人工智能
算法
数学
计算机视觉
数学分析
语言学
哲学
物理
量子力学
地震学
程序设计语言
地质学
作者
Xuezong Bai,shilong Zeng,Qiang Ma,Zihao Feng,Zongwen An
标识
DOI:10.1088/1361-6501/acc3b9
摘要
Abstract A weighted multi-neighborhood rough set (WMNRS) algorithm is designed to resolve the issue in which the neighborhood radius must be adjusted iteratively and cannot be automatically determined in the neighborhood rough set. This algorithm combined with the least squares support vector machines (LSSVM) is used for analyzing rolling bearing condition monitoring data; consequently, an intelligent fault diagnosis method is proposed. Specifically, the time-domain and frequency-domain features are extracted from the collected vibration signals to construct an original feature set. The WMNRS algorithm is then applied to screen the primary sensitive components from the constructed feature set. Finally, an optimized LSSVM is utilized to recognize the fault types. The developed method is validated on a public dataset and a measured rolling bearing dataset. The results demonstrate that the method achieves excellent diagnostic performance. Furthermore, the proposed method has some supremacy regarding running time.
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