scholarly journals Neural network fault diagnosis of a trolling motor based on feature reduction techniques for an unmanned surface vehicle

2015 ◽  
Vol 229 (8) ◽  
pp. 738-750 ◽  
Author(s):  
Wathiq Abed ◽  
Sanjay Sharma ◽  
Robert Sutton
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Feature Reduction ◽  
Unmanned Surface Vehicle ◽  
Reduction Techniques ◽  
Network Fault

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

2020 ◽  
Vol 14 (2) ◽  
pp. 205-220
Author(s):  
Yuxiu Jiang ◽  
Xiaohuan Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lower Limit ◽  
Bp Neural Network ◽  
Accelerator Pedal ◽  
Electronic Throttle ◽  
Measured Voltage ◽  
Upper Limit ◽  
Network Fault

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

Combined Optimization of Neural Network Fault Diagnosis Methods for Analog Circuits on Ships

2020 ◽  
Vol 99 (sp1) ◽  
pp. 158
Author(s):  
Sumin Guo ◽  
Hongyu Li ◽  
Bo Wu ◽  
Jingyu Zhou ◽  
Chunjian Su ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Analog Circuits ◽  
Network Fault

Based on Information Fusion Integrated Wavelet Neural Network Fault Diagnosis

2011 ◽  
Vol 219-220 ◽  
pp. 1077-1080
Author(s):  
Dong Yan Cui ◽  
Zai Xing Xie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Wavelet Neural Network ◽  
Wavelet Neural Networks ◽  
Effective Combination ◽  
Fault Diagnosis System ◽  
Network Fault ◽  
Fault Characteristic

In this paper, the integration of wavelet neural network fault diagnosis system is established based on information fusion technology. the effective combination of fault characteristic information proves that integration of wavelet neural networks make better use of a variety of characteristic information than the list of wavelet neural networks to solve difficulties and problems which are difficult to resolve by a single network.

scholarly journals Research on Fault Diagnosis Method Based on Rule Base Neural Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zheng Ni ◽  
Zhang Lin ◽  
Wang Wenfeng ◽  
Zhang Bo ◽  
Liu Yongjin ◽  
...  
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Location ◽  
Fuzzy Theory ◽  
Learning Rule ◽  
Rule Base ◽  
Fuzzy Neural ◽  
Diagnosis Method ◽  
Construction Model ◽  
Network Fault

The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

Study on Automatic Diagnosis of Remote Support for Equipment Fault

2014 ◽  
Vol 496-500 ◽  
pp. 2346-2349
Author(s):  
Xu De Cheng ◽  
Lei Lei ◽  
Jian Hu Zhang ◽  
Shi Chun Chen ◽  
Wei Peng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Service Life ◽  
Failure Rate ◽  
Technical Support ◽  
Repair Time ◽  
Automatic Diagnosis ◽  
Mechanism Of Failure ◽  
Network Fault ◽  
Equipment Performance

Repair and technical support ability of equipment are important factor restricting the persistent state of combat power. Along with the increase of service life of a certain type ordnance equipment, equipment performance become degradation, reliability decreases, failure rate increase.The purpose of this paper is to study the mechanism of failure, find out the fault rule, expert's experience with fault tree reasoning and neural network fault diagnosis technology being applied to the fault diagnosis, aiming to solve the difficult fault, shorten the repair time, improve the ability of technical support.

Research on Fault Diagnosis for Rotating Machinery Vibration of Aero-Engine Based on Wavelet Transformation and Probabilistic Neural Network

2011 ◽  
Vol 295-297 ◽  
pp. 2272-2278 ◽  
Author(s):  
Wen Jie Wu ◽  
Da Gui Huang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Decomposition ◽  
Wavelet Transformation ◽  
Probabilistic Neural Network ◽  
Mechanical Vibration ◽  
Rotating Machinery ◽  
Aero Engine ◽  
Network Fault ◽  
Fault Feature Extraction

Fault feature extraction using wavelet decomposition and probabilistic neural network fault diagnosis technology is presented in this paper. Fault diagnosis based on wavelet transformation and neural network data fusion is studied. The fault diagnosis in rotating machinery vibration of the aero-engine is simulated in Matlab. Our recent investigations demonstrate that using wavelet decomposition extract fault characteristics of the energy vector has strong generalization ability and anti-noise ability. Integration of the wavelet and neural network application can provide a better classification of diagnosis results, reliability and accuracy. This technique is suitable for the mechanical vibration fault diagnosis applications of steam turbine and gas turbine.

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