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.

Ant Colony Optimization for Neural Network

2008 ◽  
Vol 392-394 ◽  
pp. 677-681 ◽  
Author(s):  
H. Mei ◽  
Yong Wang
Keyword(s):  
Neural Network ◽  
Combinatorial Optimization ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Nonlinear Function ◽  
Continuous Optimization ◽  
Ant Colony ◽  
New Learning

A new learning way for neural network (NN) in which its weights can be optimized by using the ant colony algorithm is presented in this paper. The learning of neural network belongs to continuous optimization. The ant colony algorithm is initially developed for hard combinatorial optimization. A kind of ant colony optimization (ACO) for continuous optimization, which includes global searching, local searching and definite searching, is developed based on the basic ant colony algorithm. A three-layer neural network, as an example, is trained to express nonlinear function. The efficiency of the new algorithm is examinated. It is found that the new developed method has the merits of both ant colony algorithm and neural network.

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.

Applied-Information Technology in Short-Term Wind Speed Forecast Model for Wind Farms Based on Ant Colony Optimization and BP Neural Network

2014 ◽  
Vol 662 ◽  
pp. 259-262 ◽  
Author(s):  
Qi Di Zhao ◽  
Yang Yu ◽  
Meng Meng Jia
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Ant Colony Optimization ◽  
Bp Neural Network ◽  
Ant Colony Algorithm ◽  
Back Propagation ◽  
Wind Farms ◽  
Forecast Model ◽  
Ant Colony ◽  
Short Term

To improve the short-term wind speed forecasting accuracy of wind farms, a prediction model based on back propagation (BP) neural network combining ant colony algorithm is built to predict short-term wind speed. The input variables of BP neural network predictive model are historical wind speeds, temperature, and air pressure. Ant colony algorithm is used to optimize the weights and bias of BP neural networks. Using the ant colony optimization BP neural network model to predict the future 1h wind speed, the simulation results show that the proposed method offers the advantages of high precision and fast convergence in contrast with BP neural network.

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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