scholarly journals Diagnosis Method for Detection of Delamination of CFRP by Electric Resistance Change. Comparison of Response Surfaces and Artificial Neural Networks.

2001 ◽  
Vol 27 (4) ◽  
pp. 194-200 ◽  
Author(s):  
Atsushi IWASAKI ◽  
Akira TODOROKI
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Response Surfaces ◽  
Electric Resistance ◽  
Resistance Change ◽  
Diagnosis Method ◽  
Artificial Neural

Development of artificial neural networks based confidence intervals and response surfaces for the optimization of coagulation performance

2015 ◽  
Vol 15 (5) ◽  
pp. 1079-1087 ◽  
Author(s):  
Robert H. McArthur ◽  
Robert C. Andrews
Keyword(s):  
Carbon Dioxide ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Treatment ◽  
Confidence Intervals ◽  
Response Surfaces ◽  
Average Interval ◽  
Squared Error ◽  
Interval Width ◽  
Artificial Neural

Effective coagulation is essential to achieving drinking water treatment objectives when considering surface water. To minimize settled water turbidity, artificial neural networks (ANNs) have been adopted to predict optimum alum and carbon dioxide dosages at the Elgin Area Water Treatment Plant. ANNs were applied to predict both optimum carbon dioxide and alum dosages with correlation (R2) values of 0.68 and 0.90, respectively. ANNs were also used to developed surface response plots to ease optimum selection of dosage. Trained ANNs were used to predict turbidity outcomes for a range of alum and carbon dioxide dosages and these were compared to historical data. Point-wise confidence intervals were obtained based on error and squared error values during the training process. The probability of the true value falling within the predicted interval ranged from 0.25 to 0.81 and the average interval width ranged from 0.15 to 0.62 NTU. Training an ANN using the squared error produced a larger average interval width, but better probability of a true prediction interval.

Application of Neural Networks in Fault Diagnosis of Rotating Machinery

2007 ◽  
Author(s):  
Qing He ◽  
Dongmei Du
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Large Scale ◽  
Object Oriented ◽  
Rotating Machinery ◽  
Turbine Generator ◽  
Symptom Domains ◽  
Diagnosis Method ◽  
Artificial Neural

The fault diagnosis method based on artificial neural networks is summarized. An object-oriented paradigm is introduced to fault diagnosis for large scale rotating machinery, for example, turbine-generator. A fault diagnosis method based on object-oriented artificial neural networks for more symptom domains is presented. The training patterns are constructed. A treatment for incomplete symptom domains and/or concurrent faults in diagnosing is given. Verification is carried out for the actual turbine-generator data with incomplete symptom domains.

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