Fault Diagnosis for Closed Loop Nonlinear System Using Generalized Frequency Response Functions and Least Square Support Vector Machine

For fault diagnosis of nonlinear analog circuit, a novel method based on generalized frequency response function (GFRF) and least square support vector machine (LSSVM) classifier fusion is presented. The sinusoidal signal is used as the input of analog circuit, and then, the generalized frequency response functions are estimated directly by the time-domain formulations. The discrete Fourier transform of measurement data is avoided. After obtaining the generalized frequency response functions, the amplitudes of the GFRFs are chosen as the fault feature parameters. A classifier fusion algorithm based on least square support vector machine (LSSVM) is used for fault identification. Two LSSVM multifault classifiers with different kernel functions are constructed as subclassifiers. Fault diagnosis experiments of resistor-capacitance (RC) circuit and Sallen Key filter are carried out, respectively. The results show that the estimated GFRFs of the circuit are accurate, and the fault diagnosis method can get high recognition rate.

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Civil Aeroengine Fault Diagnosis Based on Fuzzy Least Square Support Vector Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.130-134.2047 ◽

2011 ◽

Vol 130-134 ◽

pp. 2047-2050 ◽

Cited By ~ 1

Author(s):

Hong Chun Qu ◽

Xie Bin Ding

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Support Vector Machine ◽

Fault Diagnosis ◽

Coefficient Matrix ◽

Least Square ◽

Support Vector ◽

Influence Coefficient ◽

Structural Risk ◽

Better Than

SVM(Support Vector Machine) is a new artificial intelligence methodolgy, basing on structural risk mininization principle, which has better generalization than the traditional machine learning and SVM shows powerfulability in learning with limited samples. To solve the problem of lack of engine fault samples, FLS-SVM theory, an improved SVM, which is a method is applied. 10 common engine faults are trained and recognized in the paper.The simulated datas are generated from PW4000-94 engine influence coefficient matrix at cruise, and the results show that the diagnostic accuracy of FLS-SVM is better than LS-SVM.

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A Transformer Fault Diagnosis Model Based On Hybrid Grey Wolf Optimizer and LS-SVM

Energies ◽

10.3390/en12214170 ◽

2019 ◽

Vol 12 (21) ◽

pp. 4170 ◽

Cited By ~ 5

Author(s):

Bing Zeng ◽

Jiang Guo ◽

Wenqiang Zhu ◽

Zhihuai Xiao ◽

Fang Yuan ◽

...

Keyword(s):

Support Vector Machine ◽

Fault Diagnosis ◽

Least Square ◽

Ratio Method ◽

Support Vector ◽

Grey Wolf Optimizer ◽

Local Optimum ◽

Grey Wolf ◽

Diagnosis Model ◽

Transformer Fault

Dissolved gas analysis (DGA) is a widely used method for transformer internal fault diagnosis. However, the traditional DGA technology, including Key Gas method, Dornenburg ratio method, Rogers ratio method, International Electrotechnical Commission (IEC) three-ratio method, and Duval triangle method, etc., suffers from shortcomings such as coding deficiencies, excessive coding boundaries and critical value criterion defects, which affect the reliability of fault analysis. Grey wolf optimizer (GWO) is a novel swarm intelligence optimization algorithm proposed in 2014 and it is easy for the original GWO to fall into the local optimum. This paper presents a new meta-heuristic method by hybridizing GWO with differential evolution (DE) to avoid the local optimum, improve the diversity of the population and meanwhile make an appropriate compromise between exploration and exploitation. A fault diagnosis model of hybrid grey wolf optimized least square support vector machine (HGWO-LSSVM) is proposed and applied to transformer fault diagnosis with the optimal hybrid DGA feature set selected as the input of the model. The kernel principal component analysis (KPCA) is used for feature extraction, which can decrease the training time of the model. The proposed method shows high accuracy of fault diagnosis by comparing with traditional DGA methods, least square support vector machine (LSSVM), GWO-LSSVM, particle swarm optimization (PSO)-LSSVM and genetic algorithm (GA)-LSSVM. It also shows good fitness and fast convergence rate. Accuracies calculated in this paper, however, are significantly affected by the misidentifications of faults that have been made in the DGA data collected from the literature.

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Active vibration control experiments on an AgustaWestland W30 helicopter airframe

Proceedings of the Institution of Mechanical Engineers Part C Journal of Mechanical Engineering Science ◽

10.1177/0954406211423609 ◽

2011 ◽

Vol 226 (6) ◽

pp. 1504-1516 ◽

Cited By ~ 12

Author(s):

J E Mottershead ◽

M Ghandchi Tehrani ◽

S James ◽

P Court

Keyword(s):

Vibration Control ◽

Frequency Response ◽

Closed Loop ◽

Vibration Suppression ◽

Active Vibration Control ◽

Input Voltage ◽

Open Loop ◽

Control Technique ◽

Response Functions ◽

Frequency Response Functions

This article describes the practical application of a vibration control technique, developed by the authors and known as the receptance method, to the AgustaWestland W30 helicopter airframe in the vibration test house at Yeovil. The experimental work was carried out over a total of 5 days in two visits to the Yeovil site during February and March 2011. In the experiments, existing electro-hydraulic actuators were used; they were built into the airframe structure and originally designed for vibration suppression by the methodology known as active control of structural response developed at the AgustaWestland Helicopters site in Yeovil. Accelerometers were placed at a large number of points around the airframe and an initial open-loop modal test was carried out. In a subsequent test, at higher actuator input voltage, considerable non-linearity was discovered, to the extent that the ordering of certain modes had changed. The vibration modes were, in general, heavily damped. Control was implemented using measured frequency response functions obtained at the higher input level. After acquiring the necessary measurements, simulations were carried out and the controller was implemented using MATLAB/Simulink and dSPACE. The closed-loop poles were mostly assigned with small real parts so that the system would be lightly damped and sharp peaks would be clearly apparent in the measured closed-loop frequency response functions. Locations of the open- and closed-loop poles in the complex s-plane were obtained to verify that the required assignment of poles had taken place.

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A novel analysis method for fault diagnosis of hydro-turbine governing system

Proceedings of the Institution of Mechanical Engineers Part O Journal of Risk and Reliability ◽

10.1177/1748006x16689407 ◽

2017 ◽

Vol 231 (2) ◽

pp. 164-171 ◽

Cited By ~ 4

Author(s):

Xin Xia ◽

Wei Ni ◽

Yingjun Sang

Keyword(s):

Fault Diagnosis ◽

Frequency Response ◽

Experimental Studies ◽

Response Functions ◽

Output Frequency ◽

Frequency Response Functions ◽

Hydro Turbine ◽

Identification Method ◽

Governing System ◽

Diagnosis Method

The fault diagnosis of hydro-turbine governing system is important to the operation of the hydropower station and the stability of the power grid. In order to improve the diagnostic accuracy and efficiency, a novel fault diagnosis method based on nonlinear output frequency response functions and a novel identification method of nonlinear output frequency response functions have been proposed and applied to the problem of hydro-turbine governing system fault diagnostics. First, the nonlinear model of hydro-turbine governing system is built. And the fault diagnosis principles based on nonlinear output frequency response functions are also introduced. Then, the disadvantages of the traditional identification method are discussed, and a novel identification method is proposed for nonlinear output frequency response functions according to the operation characteristic of hydro-turbine governing system. Finally, simulation verification and experimental studies have been presented to demonstrate the accuracy and efficiency of the proposed fault diagnosis method. The results indicate that the proposed method is simple and practical for fault diagnosis of hydro-turbine governing system.

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