混沌并联支持向量机及其在液压泵故障诊断中的应用

Zili Wang, Zhipeng Wang
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引用次数: 1

摘要

液压泵是液压系统的关键部件。液压泵的故障诊断对液压泵的可靠性至关重要。研究了一种混沌并行支持向量机(CPSVM),并将其应用于液压泵的故障诊断。CPSVM将混沌理论与若干支持向量机并行连接相结合。利用混沌理论的相空间重构来确定每个支持向量机的输入向量维数。每个SVM都有一个输出。每个输出的加权和被认为是CPSVM的输出。为了诊断液压泵的故障,设计了基于CPSVM的残差发生器。该残差生成器首先使用正常状态的数据进行训练。然后,通过残差分析,将其用于故障聚类。通过柱塞泵试验台对其性能和有效性进行了验证。
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Chaotic Parallel Support Vector Machine and its application for fault diagnosis of hydraulic pump
Hydraulic pump is the critical part of a hydraulic system. The diagnosis of hydraulic pump is very crucial for reliability. This paper studies on a Chaotic Parallel Support Vector Machine (CPSVM) and employs it for fault diagnosis of hydraulic pump. The CPSVM combines the chaos theory and a number of SVMs connected in parallel. Phase-space reconstruction of chaos theory is utilized to determine the dimension of input vectors for each SVM. Each SVM has an output. A weighted sum of each output is considered as the output of the CPSVM. To diagnose faults of hydraulic pump, a residual error generator is designed based on the CPSVM. This residual error generator is firstly trained using data from normal state. Then, it can be used for fault clustering by analysis of the residual error. Its performance and effectiveness has also been validated via a plunger pump test-bed.
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