Research on Fault Diagnosis Method of Electric Vehicle Battery System Based on Wavelet-RBF Neural Network

To improve the ability of fault diagnosis for mechanical equipment, a Radial Basis Function Neural Network (RBFNN) diagnosis method based on Unscented Kalman Filter (UKF) algorithm is proposed. In the algorithm, at first, UKF algorithm is used to estimate the parameters of RBFNN, and then the proposed method is introduced into the fault diagnosis of mechanical equipment. The simulation indicates that the established model has a good diagnosis performance for mechanical fault diagnosis.

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Fault Diagnosis of Cascaded Converter Based on Wavelet Packet RBF Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.273.300 ◽

2013 ◽

Vol 273 ◽

pp. 300-304

Author(s):

Xin Wang ◽

Juan Xu ◽

Guo Dong Zhang ◽

Rui Min Qi

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Learning Algorithm ◽

Rbf Neural Network ◽

Wavelet Packet ◽

Open Circuit ◽

Learning Performance ◽

Cascaded Converter ◽

Diagnosis Method ◽

Faults Diagnosis

To study the power component open circuit faults diagnosis method of the cascaded converter. Aiming at the insufficiency of the BP learning algorithm in the machinery fault diagnosis, such as the low learning convergence speed, the easily appearing local minimum, the instability learning performance caused by the initial value, to proposed a new method applied to the cascaded converter based on radial basis function (RBF) neural network. Experiments show that the method based on wavelet packet analysis and RBF neural network has better learning and fault identification capability, and it can meet the online real-time fault diagnosis of the cascaded converter.

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A Fault Diagnosis Method for Lithium-Ion Battery Packs Using Improved RBF Neural Network

Frontiers in Energy Research ◽

10.3389/fenrg.2021.702139 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jia Wang ◽

Shenglong Zhang ◽

Xia Hu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Fault Diagnosis ◽

Lithium Ion Battery ◽

Electric Vehicles ◽

Rbf Neural Network ◽

Lithium Ion ◽

Rbf Neural Networks ◽

Battery Packs ◽

Diagnosis Method

With the increasing demand for electric vehicles, the high voltage safety of electric vehicles has attracted significant attention. More than 30% of electric vehicle accidents are caused by the battery system; hence, it is vital to investigate the fault diagnosis method of lithium-ion battery packs. The fault types of lithium-ion battery packs for electric vehicles are complex, and the treatment is cumbersome. This paper presents a fault diagnosis method for the electric vehicle power battery using the improved radial basis function (RBF) neural network. First, the fault information of lithium-ion battery packs was collected using battery test equipment, and the fault levels were then determined. Subsequently, the improved RBF neural networks were employed to identify the fault of the lithium-ion battery pack system using the experimental data. The diagnosis test results showed that the improved RBF neural networks could effectively identify the fault diagnosis information of the lithium-ion battery packs, and the diagnosis accuracy was about 100%.

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Data-Driven Based Gas Path Fault Diagnosis for Turbo-Shaft Engine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.249-250.400 ◽

2012 ◽

Vol 249-250 ◽

pp. 400-404 ◽

Cited By ~ 2

Author(s):

Feng Lu ◽

Tie Bin Zhu ◽

Yi Qiu Lv

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Information Fusion ◽

Rbf Neural Network ◽

Evidence Theory ◽

Aircraft Engine ◽

Data Driven ◽

Support Vector ◽

Diagnosis Method ◽

Gas Path Fault

In order to improve diagnostic accuracy and reduce the rate of misdiagnosis to the aircraft engine gas path faulty, the methods based on data-driven and information fusion are developed and analyzed. BP neural network (NN) and RBF neural network based on data-driven single gas path fault diagnosis method is introduced firstly. Design gas path performance estimators and the fault type classification for turbo-shaft engine. Then the gas path fused diagnostic structure based on D-S evidence theory and least squares support vector machine are developed. Comparisons of the turbo-shaft engine gas path fault diagnosis verify the feasibility and effectiveness of the gas path fault diagnosis based on information fusion.

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The Engine Fault Diagnosis with RBFNN Based on AIC

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.468-471.1066 ◽

2012 ◽

Vol 468-471 ◽

pp. 1066-1069

Author(s):

Qiang Huang ◽

Xiao Zhuo Ouyang ◽

Cheng Wang

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Diesel Engine ◽

Rbf Neural Network ◽

Information Criterion ◽

Vibration Signals ◽

The Neural Network ◽

Rotary Machines ◽

Diagnosis Method ◽

Diagnosis Model

In this paper, an engine diagnosis method with high precision and quickly response is proposed. Firstly, the Akaike Information Criterion (AIC) is used to improve the performance of the neural network to build the fault diagnosis model. Then the vibration signals are analyzed to estimate the states of the diesel engine. Finally, the five states of diesel engine are set to validate the veracity of diagnosis method. According to experiment and simulation researches, it indicates that the diagnosis method with RBF neural network based on AIC is effective. The veracity of identification is 100% to the single fault. It is a valuable reference to the vibration diagnosis for other complex rotary machines.

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A novel hybrid intelligent fault diagnosis method based on improved RBF neural network

Proceedings of the 2016 International Conference on Economics and Management Innovations ◽

10.2991/icemi-16.2016.49 ◽

2016 ◽

Author(s):

Yi Liu

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Rbf Neural Network ◽

Intelligent Fault Diagnosis ◽

Diagnosis Method

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Stream Turbine Vibration Fault Diagnosis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.340.90 ◽

2013 ◽

Vol 340 ◽

pp. 90-94 ◽

Cited By ~ 1

Author(s):

Hong Sheng Su

Keyword(s):

Neural Network ◽

Neural Networks ◽

Fault Diagnosis ◽

Rbf Neural Network ◽

Local Extremum ◽

Decision Table ◽

Rbf Neural Networks ◽

Global Extremum ◽

Artificial Fish Swarm Algorithm ◽

Diagnosis Method

RBF neural networks possessed the excellent characteristics such as insensitive on the initial weights and parameters with artificial fish-swarm algorithm (AFSA) applied, which made it have abilities to get rid of the local extremum and obtain the global extremum, and called as AFSA-RBF neural networks. In this paper, a new stream turbine vibration fault diagnosis method was presented based on AFSA-RBF neural networks. After quantification and reduction of the diagnosis decision table, the simplified decision table served as the learning samples of AFSA-RBF neural network, and the well-trained neural network was then applied to diagnose stream turbine vibration faults. The diagnosis results show that the proposed method possesses higher convergence speed and diagnosis precision, and is a very effective turbine fault diagnosis method.

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