ART-Artificial Immune Network and Application in Fault Diagnosis of the Reciprocating Compressor

2006 ◽  
pp. 502-505
Author(s):  
Maolin Li ◽  
Na Wang ◽  
Haifeng Du ◽  
Jian Zhuang ◽  
Sun’an Wang
Keyword(s):  
Fault Diagnosis ◽  
Immune Network ◽  
Reciprocating Compressor ◽  
Artificial Immune ◽  
Artificial Immune Network

Research on Transformer Fault Diagnosis Method Based on Artificial Immune Network and Fuzzy C-Means Clustering Algorithm

2014 ◽  
Vol 574 ◽  
pp. 468-473 ◽  
Author(s):  
Fu Zhong Wang ◽  
Shu Min Shao ◽  
Peng Fei Dong
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Power Transformer ◽  
Immune Network ◽  
Artificial Immune ◽  
Fuzzy C Means ◽  
Artificial Immune Network ◽  
Fuzzy C Means Clustering ◽  
Diagnosis Method ◽  
Transformer Fault

The transformer is one of the indispensable equipment in transformer substation, it is of great significance for fault diagnosis. In order to accurately judge the transformer fault types, an algorithm is proposed based on artificial immune network combined with fuzzy c-means clustering to study on transformer fault samples. Focus on the introduction of data processing of transformer faults based on artificial immune network, the identification of transformer faults based on fuzzy c-means clustering, and the simulation process. The experimental results show that the proposed algorithm can classify power transformer fault types effectively, and the algorithm has a good application prospect in the transformer fault diagnosis.

A Fault Diagnosis Method Based on Artificial Immune Network

2013 ◽  
Vol 385-386 ◽  
pp. 658-662 ◽  
Author(s):  
Wen Ke Jiang ◽  
Yu Juan Chen ◽  
Jing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Success Rate ◽  
Basis Function ◽  
Immune Network ◽  
Artificial Immune ◽  
Training Samples ◽  
Artificial Immune Network ◽  
Diagnosis Method ◽  
Gear Case ◽  
Hidden Layer

A new fault diagnosis method based on artificial immune network is proposed. The network combined aiNet with radial basis function (RBF) NN. The structure of the network proposed is the same as RBF NN. The training samples are clustered first by the improved aiNet algorithm. The centers of the clustering are saved as the centers of the hidden layer, therefore, the amount and positions of nodes in the hidden layer can be determined automatically. The weight matrix is determined by least squares (LS) algorithm. The network is applied to fault diagnosis of CJK6136 spindle gear case. The results of the experiments confirm the performance of the proposed network through comparing with RBF NN under the same conditions. The diagnosis success rate for the network proposed was 99%, while that for RBF NN is 89.5%.

Identification of cavitation in centrifugal pump by artificial immune network

2021 ◽  
pp. 095440892110284
Author(s):  
Seyed M Matloobi ◽  
Mohammad Riahi
Keyword(s):  
Immune System ◽  
Production Systems ◽  
Principal Component ◽  
Immune Network ◽  
Centrifugal Pumps ◽  
Artificial Immune ◽  
Human Immune System ◽  
Time Frequency ◽  
Artificial Immune Network ◽  
Damage Condition

Reducing the cost of unscheduled shutdowns and enhancing the reliability of production systems is an important goal for various industries; this could be achieved by condition monitoring and artificial intelligence. Cavitation is a common undesired phenomenon in centrifugal pumps, which causes damage and its detection in the preliminary stage is very important. In this paper, cavitation is identified by use of vibration and current signal and artificial immune network that is modeled on the base of the human immune system. For this purpose, first data collection were done by a laboratory setup in health and five stages damage condition; then various features in time, frequency, and time–frequency were extracted from vibration and current signals in addition to pressure and flow rate; next feature selection and dimensions reduction were done by artificial immune method to use for classification; finally, they were used by artificial immune network and some other methods to identify the system condition and classification. The results of this study showed that this method is more accurate in the detection of cavitation in the initial stage compared to methods such as non-linear supportive vector machine, multi-layer artificial neural network, K-means and fuzzy C-means with the same data. Also, selected features with artificial immune system were better than principal component analysis results.

scholarly journals An Associate Rules Mining Algorithm Based on Artificial Immune Network for SAR Image Segmentation

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Mengling Zhao ◽  
Hongwei Liu
Keyword(s):  
Association Rules ◽  
Large Scale ◽  
Classification Algorithm ◽  
Data Sets ◽  
Immune Network ◽  
Artificial Immune ◽  
Mining Algorithm ◽  
Sar Image Segmentation ◽  
Artificial Immune Network ◽  
Adaptive Pso

As a computational intelligence method, artificial immune network (AIN) algorithm has been widely applied to pattern recognition and data classification. In the existing artificial immune network algorithms, the calculating affinity for classifying is based on calculating a certain distance, which may lead to some unsatisfactory results in dealing with data with nominal attributes. To overcome the shortcoming, the association rules are introduced into AIN algorithm, and we propose a new classification algorithm an associate rules mining algorithm based on artificial immune network (ARM-AIN). The new method uses the association rules to represent immune cells and mine the best association rules rather than searching optimal clustering centers. The proposed algorithm has been extensively compared with artificial immune network classification (AINC) algorithm, artificial immune network classification algorithm based on self-adaptive PSO (SPSO-AINC), and PSO-AINC over several large-scale data sets, target recognition of remote sensing image, and segmentation of three different SAR images. The result of experiment indicates the superiority of ARM-AIN in classification accuracy and running time.

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