A two‐phase features extraction approach for BRB based fault diagnosis of electromechanical system

IF 3.9 4区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Adaptive Control and Signal Processing Pub Date : 2024-06-25 DOI:10.1002/acs.3862

Zhenjie Zhang, Wenchao Liu, Gang Xiao, Xiaobin Xu, Meng Li, Zhenbo Cheng, Yuanming Zhang, Wenming Xu, Leilei Chang

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引用次数: 0

Abstract

SummaryBelief rule base (BRB) is an effective nonlinear relationship modeling approach. It has been widely used in the fault diagnosis of electromechanical systems. To improve the performance of the BRB‐based diagnostic model, a two‐phase features extraction approach called CNPCA based on complex network (CN) and principal component analysis (PCA) is proposed in this paper. In the first phase, the weighted visibility graph method is applied to transform the time series data of monitored variables into complex networks. Then the statistical attributes of the constructed networks are extracted as the initial features. In the second phase, the PCA method is used to process the initial features and the principal component features are obtained. After that, the CNPCA‐BRB diagnostic model for the electromechanical system is constructed. The experimental results of the elevator fault diagnosis show that the constructed diagnostic model outperforms better than the classical ones. It demonstrates that the CNPCA approach can ensure the integrity of fault information in the features and improve the separability of the fault features.

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基于 BRB 的机电系统故障诊断两阶段特征提取方法

摘要信念规则库（BRB）是一种有效的非线性关系建模方法。它已被广泛应用于机电系统的故障诊断。为了提高基于信念规则库的诊断模型的性能，本文提出了一种基于复杂网络（CN）和主成分分析（PCA）的两阶段特征提取方法，即 CNPCA。在第一阶段，应用加权可见性图法将监测变量的时间序列数据转化为复杂网络。然后提取所构建网络的统计属性作为初始特征。在第二阶段，使用 PCA 方法处理初始特征并获得主成分特征。之后，构建机电系统的 CNPCA-BRB 诊断模型。电梯故障诊断的实验结果表明，所构建的诊断模型优于传统诊断模型。这表明 CNPCA 方法可以确保特征中故障信息的完整性，并提高故障特征的可分离性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Adaptive Control and Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

16.10%

发文量

163

审稿时长

5 months

期刊介绍： The International Journal of Adaptive Control and Signal Processing is concerned with the design, synthesis and application of estimators or controllers where adaptive features are needed to cope with uncertainties.Papers on signal processing should also have some relevance to adaptive systems. The journal focus is on model based control design approaches rather than heuristic or rule based control design methods. All papers will be expected to include significant novel material. Both the theory and application of adaptive systems and system identification are areas of interest. Papers on applications can include problems in the implementation of algorithms for real time signal processing and control. The stability, convergence, robustness and numerical aspects of adaptive algorithms are also suitable topics. The related subjects of controller tuning, filtering, networks and switching theory are also of interest. Principal areas to be addressed include: Auto-Tuning, Self-Tuning and Model Reference Adaptive Controllers Nonlinear, Robust and Intelligent Adaptive Controllers Linear and Nonlinear Multivariable System Identification and Estimation Identification of Linear Parameter Varying, Distributed and Hybrid Systems Multiple Model Adaptive Control Adaptive Signal processing Theory and Algorithms Adaptation in Multi-Agent Systems Condition Monitoring Systems Fault Detection and Isolation Methods Fault Detection and Isolation Methods Fault-Tolerant Control (system supervision and diagnosis) Learning Systems and Adaptive Modelling Real Time Algorithms for Adaptive Signal Processing and Control Adaptive Signal Processing and Control Applications Adaptive Cloud Architectures and Networking Adaptive Mechanisms for Internet of Things Adaptive Sliding Mode Control.