Abstract- A fault diagnosis method based on adaptive dynamic clone selection neural network (ADCSNN) is proposed in this paper. In this method the weights of neural network is encoded as the antibody, and the network error is considered as the antigen. The algorithm is then applied to fault detection of motor equipment. The experiments results show that the fault diagnosis method based on ADCS neural network has the capability in escaping local minimum and improving the algorithm speed, this gives better performance
In order to identify any decrease in efficiency and any loss in industrial application a suitable mo...
Fault detection and diagnosis is currently a very important problem in induction machine management....
Fault detection and diagnosis is currently a very important problem in induction machine management....
In this paper an artificial neural network based technique will be introduce, which is capable to s...
Current signal monitoring (CSM) can be used as an effective tool for diagnosing broken rotor bars fa...
This article proposes a new solution method for diagnosing faults in a multi phase induction motor u...
The paper deals with the analysis of an Artificial Neural Network (ANN) approach suitable for on - l...
Machine malfunctions are pestilence to all production lines. One fault or malfunction leads to anoth...
An improvement of induction machine rotor fault diagnosis based on neural network approach is presen...
In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature...
International audienceTraction Motors Condition Monitoring is one of the important factors in increa...
Most of intelligent control in movement control involves fuzzy logic and neural network systems. In ...
Abstract. This paper presents a motor fault diagnosis method based on negative selection algorithm. ...
This paper proposes an online fault diagnosis system for induction motors through the combination of...
Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical ...
In order to identify any decrease in efficiency and any loss in industrial application a suitable mo...
Fault detection and diagnosis is currently a very important problem in induction machine management....
Fault detection and diagnosis is currently a very important problem in induction machine management....
In this paper an artificial neural network based technique will be introduce, which is capable to s...
Current signal monitoring (CSM) can be used as an effective tool for diagnosing broken rotor bars fa...
This article proposes a new solution method for diagnosing faults in a multi phase induction motor u...
The paper deals with the analysis of an Artificial Neural Network (ANN) approach suitable for on - l...
Machine malfunctions are pestilence to all production lines. One fault or malfunction leads to anoth...
An improvement of induction machine rotor fault diagnosis based on neural network approach is presen...
In this paper, broken rotor bar (BRB) fault is investigated by utilizing the Motor Current Signature...
International audienceTraction Motors Condition Monitoring is one of the important factors in increa...
Most of intelligent control in movement control involves fuzzy logic and neural network systems. In ...
Abstract. This paper presents a motor fault diagnosis method based on negative selection algorithm. ...
This paper proposes an online fault diagnosis system for induction motors through the combination of...
Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical ...
In order to identify any decrease in efficiency and any loss in industrial application a suitable mo...
Fault detection and diagnosis is currently a very important problem in induction machine management....
Fault detection and diagnosis is currently a very important problem in induction machine management....