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.

Fault diagnosis based on support vector machines with parameter optimization by an ant colony algorithm

2009 ◽  
Vol 224 (1) ◽  
pp. 217-229 ◽  
Author(s):  
X L Zhang ◽  
X F Chen ◽  
Z J He
Keyword(s):  
Fault Diagnosis ◽  
Support Vector Machines ◽  
Parameter Optimization ◽  
Ant Colony Algorithm ◽  
Small Sample ◽  
Ant Colony ◽  
Support Vector ◽  
Optimal Parameters ◽  
Vector Machines ◽  
Diagnosis Method

Since support vector machines (SVM) exhibit a good generalization performance in the small sample cases, these have a wide application in machinery fault diagnosis. However, a problem arises from setting optimal parameters for SVM so as to obtain optimal diagnosis result. This article presents a fault diagnosis method based on SVM with parameter optimization by ant colony algorithm to attain a desirable fault diagnosis result, which is performed on the locomotive roller bearings to validate its feasibility and efficiency. The experiment finds that the proposed algorithm of ant colony optimization with SVM (ACO—SVM) can help one to obtain a good fault diagnosis result, which confirms the advantage of the proposed ACO—SVM approach.

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.

Transformer Fault Diagnosis Method Based on Support Vector Machine and Ant Colony

2013 ◽  
Vol 659 ◽  
pp. 54-58 ◽  
Author(s):  
Li Li Mo
Keyword(s):  
Fault Diagnosis ◽  
Ant Colony Algorithm ◽  
Cross Validation ◽  
Gas Analysis ◽  
Ant Colony ◽  
Support Vector ◽  
Dissolved Gas ◽  
Wide Range ◽  
Diagnosis Method ◽  
Transformer Fault

For transformer fault diagnosis of the IEC three-ratio is an effective method in the dissolved gas analysis (DGA). But it does not offer completely objective, accurate diagnosis for all the faults. Aiming at parameters are confirmed by the cross validation, using the ant colony algorithm, the ACSVM-IEC method for the transformer fault diagnosis is proposed. Experimental results show that the proposed algorithm in this paper that can find out the optimum accurately in a wide range. The proposed approach is robust and practical for transformer fault diagnosis.

ACO Trained ANN-Based Intelligent Fault Diagnosis

2010 ◽  
Vol 20-23 ◽  
pp. 141-146
Author(s):  
Hua Wang Shi
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Nonlinear Function ◽  
Rolling Bearing ◽  
Ant Colony ◽  
Traditional Algorithm ◽  
Artificial Neural Network Ann ◽  
Intelligent Model

By use of the properties of ant colony algorithm and particle swarm optimization, this paper presents an application of an Ant Colony Optimization (ACO) algorithm and artificial neural network (ANN) to fault diagnosis. The ACO algorithm is a novel heuristic bionic algorithm, which is based on the behavior of real ants in nature searching for food. Neural network is used to express the nonlinear function between the input and output of the fault diagnosis of the rolling bearing. And ant colony optimization (ACO) algorithm is used to learn NN. The new algorithm has the merits of both ACO algorithm and neural network. It also provides a new way for the fault diagnosis through constructing the intelligent model by ant colony-neural network and overcomes the shortcomings of traditional algorithm.

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.

Application of Ant Colony Algorithm in Fault Diagnosis of Roller Bearing

2011 ◽  
Vol 291-294 ◽  
pp. 1957-1960 ◽  
Author(s):  
Liu Yang Song ◽  
Hua Qing Wang ◽  
Jia Pan ◽  
Jin Ji Gao ◽  
Ke Li
Keyword(s):  
Fault Diagnosis ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Clustering Algorithm ◽  
Roller Bearing ◽  
Ant Colony ◽  
Vibration Signals ◽  
Clustering Problem ◽  
Diagnosis Method ◽  
Condition Diagnosis

This paper presents a condition diagnosis method for a roller bearing using the ant colony optimization (ACO). The symptom parameters in frequency domain are considered for reflecting the feature of vibration signals measured under different states. The states identification for machinery diagnosis is converted to clustering problem of different states. The distance-based diagnosis method, which distinguishes the machinery states by comparing the distance, is proposed using the ACO clustering algorithm and utilized to detect faults and recognize fault types. The analysis results demonstrate that the proposed method can recognize the faults types effectively.

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