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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >A Novel Method for Fault Diagnosis of the Two-Input Two-Output Nonlinear Mass-Spring-Damper System Based on NOFRF and MBPCA
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A Novel Method for Fault Diagnosis of the Two-Input Two-Output Nonlinear Mass-Spring-Damper System Based on NOFRF and MBPCA

机译：基于NOFRF和MBPCA的两输入两输出非线性质量弹簧阻尼系统故障诊断的一种新方法

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摘要

For fault diagnosis of the two-input two-output mass-spring-damper system, a novel method based on the nonlinear output frequency response function (NOFRF) and multiblock principal component analysis (MBPCA) is proposed. The NOFRF is the extension of the frequency response function of the linear system to the nonlinear system, which can reflect the inherent characteristics of the nonlinear system. Therefore, the NOFRF is used to obtain the original fault feature data. In order to reduce the amount of feature data, a multiblock principal component analysis method is used for fault feature extraction. The least squares support vector machine (LSSVM) is used to construct a multifault classifier. A simplified LSSVM model is adopted to improve the training speed, and the conjugate gradient algorithm is used to reduce the required storage of LSSVM training. A fault diagnosis simulation experiment of a two-input two-output mass-spring-damper system is carried out. The results show that the proposed method has good diagnosis performance, and the training speed of the simplified LSSVM model is significantly higher than the traditional LSSVM.
机译：对于两输入两输出质量弹簧阻尼系统的故障诊断，提出了一种基于非线性输出频率响应函数（NOFRF）和多块主成分分析（MBPCA）的新型方法。 NoFRF是线性系统到非线性系统的频率响应功能的延伸，这可以反映非线性系统的固有特性。因此，NofRF用于获得原始故障特征数据。为了减少特征数据​​的量，多块主成分分析方法用于故障特征提取。最小二乘支持向量机（LSSVM）用于构造多级分类器。采用简化的LSSVM模型来提高训练速度，并使用共轭梯度算法来减少LSSVM培训所需的存储。进行了双输入两输出质量弹簧阻尼系统的故障诊断模拟实验。结果表明，该方法具有良好的诊断性能，简化LSSVM模型的训练速度明显高于传统的LSSVM。

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