The Research of Armored Vehicles Weapon System Fault Diagnosis Method

2014 ◽  
Vol 678 ◽  
pp. 309-312
Author(s):  
Hai Feng Xu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Tree ◽  
Original Data ◽  
Tree Model ◽  
Difficult Situation ◽  
The Neural Network ◽  
Diagnosis Method ◽  
Armored Vehicles ◽  
Neural Network Technology

For armored vehicles electrical system fault diagnosis of fault original data collection difficult situation, this paper introduces the fault diagnosis technology based on fault tree model and fault diagnosis based on neural network technology, and the two kinds of fusion technology, complement each other, with a certain type of equipment control system as an example the case analysis, illustrates the fault tree of the neural network and the rationality and validity of the integrated fault diagnosis thinking.

Fault Diagnosis Research of Hydraulic Excavator Based on Fault Tree and Fuzzy Neural Network

2013 ◽  
Vol 303-306 ◽  
pp. 1350-1356
Author(s):  
Guo Ping Li ◽  
Qing Wei Zhang ◽  
Ma Xiao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fuzzy Neural Network ◽  
Fault Tree ◽  
Experimental Result ◽  
Hydraulic Excavator ◽  
Tree Model ◽  
Neural Network Learning ◽  
Fuzzy Neural ◽  
Diagnosis Method

Directing to the dispersiveness and faintness failure characteristics of hydraulic excavator, the fault diagnosis method was presented based on the fault tree and fuzzy neural network. On the basis of analysis of the hydraulic excavator system works, the fault tree model of hydraulic excavator was built by using fault diagnosis tree. And then, utilizing the example of hydraulic excavator fault diagnosis, the method of building neural network, obtaining training samples and neural network learning in the process of intelligent fault diagnosis are expounded. And the status monitoring data of hydraulic excavator was used as the sample data source. Using fuzzy logic methods the samples were blurred. The fault diagnosis of hydraulic excavator was achieved with BP neural network. The experimental result demonstrated that the information of sign failure was fully used through the algorithm. The algorithm was feasible and effective to fault diagnosis of hydraulic excavator. A new diagnosis method was proposed for fault diagnosis of other similar device.

Application of Ant Colony Algorithms and Neural Networks in Fault Diagnosis

2014 ◽  
Vol 556-562 ◽  
pp. 3014-3017
Author(s):  
Jing Bo Yu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Ant Colony Algorithm ◽  
Ant Colony ◽  
Ant Colony Algorithms ◽  
Weak Points ◽  
Diagnosis Method ◽  
Computational Simplicity ◽  
Minimum Points ◽  
Neural Network Technology

Neural network technology is widely applied due to its computational simplicity and versatility. But, this method has some weak points, for example, slow convergence, less accurate and easy to fall into local minimum points. Combined ant colony algorithm and neural network for fault diagnosis, it can overcome the limitations of a single fault diagnosis method. Ant colony neural network method is applied to gearbox fault diagnosis, the results show that the diagnosis with characteristics of high precision, strong scientific and practical wider.

scholarly journals Data-Driven Fault Diagnosis Method for Power Transformers Using Modified Kriging Model

2017 ◽  
Vol 2017 ◽  
pp. 1-5
Author(s):  
Yu Ding ◽  
Qiang Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Kriging Model ◽  
Data Driven ◽  
Power Transformers ◽  
Global Approximation ◽  
The Neural Network ◽  
Modeling Accuracy ◽  
Diagnosis Method ◽  
Transformer Fault

A data-driven fault diagnosis method that combines Kriging model and neural network is presented and is further used for power transformers based on analysis of dissolved gases in oil. In order to improve modeling accuracy of Kriging model, a modified model that replaces the global model of Kriging model with BP neural network is presented and is further extended using linearity weighted aggregation method. The presented method integrates characteristics of the global approximation of the neural network technology and the localized departure of the Kriging model, which improves modeling accuracy. Finally, the validity of this method is demonstrated by several numerical computations of transformer fault diagnosis problems.

Wavelet Analysis of Fault Diagnosis Technology

2014 ◽  
Vol 651-653 ◽  
pp. 2402-2405
Author(s):  
Jia Tian
Keyword(s):  
Neural Network ◽  
Signal Processing ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Computer Technology ◽  
Wavelet Function ◽  
Network Technology ◽  
Knowledge Based ◽  
Diagnosis Method ◽  
Neural Network Technology

In recent years, the development of computer technology, signal processing, artificial intelligence, pattern recognition technology; and promote the continuous development of fault diagnosis technology, especially knowledge-based fault diagnosis method has been widely studied. Which, along with the increasingly improved neural network technology, the fault diagnosis method based on neural network has been widespread concern. Since one of the main steps of fault diagnosis is signal processing, while wavelet analysis is an effective tool to process signals and wavelet function has many good characteristics, so the combination of wavelet and neural network, so called wavelet neural network, has become a focus in fault diagnosis field recently.

scholarly journals Research on Fault Diagnosis Expert System Based on the Neural Network and the Fault Tree Technology

2012 ◽  
Vol 31 ◽  
pp. 1206-1210 ◽  
Author(s):  
Yingying Wang ◽  
Qiuju Li ◽  
Ming Chang ◽  
Hongwei Chen ◽  
Guohua Zang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Expert System ◽  
Fault Tree ◽  
The Neural Network

Research on fault diagnosis method of rapier loom based on the fusion of expert system and fault tree

2021 ◽  
pp. 1-13
Author(s):  
Yanjun Xiao ◽  
Furong Han ◽  
Yvheng Ding ◽  
Weiling Liu
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Base ◽  
Fault Tree ◽  
Maintenance Cost ◽  
Intelligent Fault Diagnosis ◽  
Tree Model ◽  
Intelligent Diagnosis ◽  
Diagnosis Method ◽  
System Knowledge

The safety and stability of the rapier loom during operation directly impact the quality of the fabric. Therefore, it is of great significance to carry out fault diagnosis research on rapier looms. In order to solve the problems of low diagnosis efficiency, untimely diagnosis, and high maintenance cost of existing rapier looms in manual troubleshooting of loom failures. This paper proposes a new intelligent fault diagnosis method for rapier looms based on the fusion of expert system and fault tree. A new expert system knowledge base is formed by combining the dynamic fault tree model with the expert system knowledge base. It solves the problem that the traditional expert system cannot achieve precise positioning in the face of complex fault types. Construct the rapier loom’s fault diagnosis model, build the intelligent diagnosis platform, and finally realize the intelligent fault diagnosis of the rapier loom. Experimental results show that the algorithm can quickly diagnose and locate rapier loom faults. Compared with the current intelligent diagnosis algorithm, the algorithm structure is simplified, which provides a theoretical basis for the broad application of intelligent fault diagnosis on rapier looms.

The Engine Fault Diagnosis with RBFNN Based on AIC

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.

Research on Intelligent Fault Diagnosis Methods of Armored Vehicles Electrical System

2013 ◽  
Vol 753-755 ◽  
pp. 2175-2178
Author(s):  
Lu Wang ◽  
Gui You Lu ◽  
Dan Li ◽  
Guo Bao Ding
Keyword(s):  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Fault Location ◽  
Fault Simulation ◽  
Original Data ◽  
Electrical System ◽  
Difficult Situation ◽  
Intelligent Computing ◽  
Armored Vehicles ◽  
Logic Reasoning

For armored vehicles electrical system fault diagnosis of fault original data collection difficult situation, a new intelligent computing programs designed based on the fuzzy set theory and possibility distribution theory and fuzzy logic reasoning design, which realized the process of KA automatization through the combination of fault simulation technology and knowledge acquisition technology. The approach presented in this paper makes the work of knowledge acquisition (KA) engineer easier, and makes fast diagnosis fault location and fault reasons possible.

scholarly journals Multi-Sensor Fault Diagnosis of Underwater Thruster Propeller Based on Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7187
Author(s):  
Chia-Ming Tsai ◽  
Chiao-Sheng Wang ◽  
Yu-Jen Chung ◽  
Yung-Da Sun ◽  
Jau-Woei Perng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rapid Development ◽  
Sensor Fault ◽  
The Neural Network ◽  
Signal Input ◽  
Diagnosis Method ◽  
The One ◽  
The Time Domain ◽  
Single Neural Network

With the rapid development of unmanned surfaces and underwater vehicles, fault diagnoses for underwater thrusters are important to prevent sudden damage, which can cause huge losses. The propeller causes the most common type of thruster damage. Thus, it is important to monitor the propeller’s health reliably. This study proposes a fault diagnosis method for underwater thruster propellers. A deep convolutional neural network was proposed to monitor propeller conditions. A Hall element and hydrophone were used to obtain the current signal from the thruster and the sound signal in water, respectively. These raw data were fast Fourier transformed from the time domain to the frequency domain and used as the input to the neural network. The output of the neural network indicated the propeller’s health conditions. This study demonstrated the results of a single signal and the fusion of multiple signals in a neural network. The results showed that the multi-signal input had a higher accuracy than the one-signal input. With multi-signal inputs, training two types of signals with a separated neural network and then merging them at the end yielded the best results (99.88%), as compared to training two types of signals with a single neural network.

scholarly journals Fault Diagnosis for Methane Sensors using Generalized Regression Neural Network

2016 ◽  
Vol 12 (03) ◽  
pp. 42 ◽  
Author(s):  
Kaifeng Huang ◽  
Zegong Liu ◽  
Dan Huang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Residual Error ◽  
Generalized Regression Neural Network ◽  
Error Signal ◽  
The Neural Network ◽  
Diagnosis Method ◽  
Sensor Information ◽  
Generalized Regression

To identify the hang, collision and drift faults of methane sensors, this paper presents a fault diagnosis method for methane sensors using multi-sensor information fusion. A methane concentration monitoring approximation model with multi-sensor information fusion is established based on generalized regression neural network (GRNN).The output of the neural network is compared with the measured value of the sensor to be diagnosed to obtain the variation curve of the residual error signal. Through the analysis of the variation tendency of the residual error signal, the fault status of a methane sensor could be determined based on a reasonable threshold. Through simulation comparison is applied between the two models of GRNN and BP neural network; verify the GRNN model is much more precise in the approximation of methane concentrations. Fault diagnosis for methane sensors using generalized regression neural network is effective and more efficient.

Sign in / Sign up

Export Citation Format

Share Document