利用光滑变结构滤波器训练的神经网络进行发动机故障检测

R. Ahmed, M. E. Sayed, S. Gadsden, S. Habibi
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引用次数: 6

摘要

多层神经网络是一个多输入、多输出(MIMO)的非线性系统,其训练可以看作是一个通过估计网络权值来估计非线性参数的问题。本文将较新的光滑变结构滤波器(SVSF)用于非线性多层前馈网络的训练。SVSF是一种具有预测-校正形式的递归滑模参数和状态估计器。利用切换增益,计算校正项,迫使网络权值收敛到最优权值的一个邻域内。利用基于svm的训练神经网络对发动机振动数据进行故障分类。四冲程八缸发动机有两个故障。此外,对流行的反向传播方法、扩展卡尔曼滤波(EKF)和支持向量滤波进行了比较研究。实验结果表明,SVSF与EKF具有可比性,两种方法都优于反向传播。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fault detection of an engine using a neural network trained by the smooth variable structure filter
A multilayered neural network is a multi-input, multi-output (MIMO) nonlinear system in which training can be regarded as a nonlinear parameter estimation problem by estimating the network weights. In this paper, the relatively new smooth variable structure filter (SVSF) is used for the training of a nonlinear multilayered feed forward network. The SVSF is a recursive sliding mode parameter and state estimator that has a predictor-corrector form. Using a switching gain, a corrective term is calculated to force the network weights to converge to within a neighbourhood of the optimal weight values. SVSF-based trained neural networks are used to classify engine faults on the basis of vibration data. Two faults are induced in a four-stroke, eight-cylinder engine. Furthermore, a comparative study between the popular back propagation method, the extended Kalman filter (EKF), and the SVSF is presented. Experimental results indicate that the SVSF is comparable with the EKF, and both methods outperform back propagation.
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