Evaluation of engine performance and emission of African pear seed oil ( APO ) biodiesel and its prediction via multi‐input‐multi‐output artificial neural network ( ANN ) and sensitivity analysis

2021 ◽  
Author(s):  
Callistus Nonso Ude ◽  
O Dominic Onukwuli ◽  
N Nneka Uchegbu ◽  
Jonah C Umeuzuegbu ◽  
Ndidi F Amulu
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Seed Oil ◽  
Engine Performance ◽  
Performance And Emission ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
African Pear

scholarly journals Artificial Neural Network coupled Condition Monitoring for advanced Fault Diagnosis of Engine

2021 ◽  
Author(s):  
Suvendu Mohanty ◽  
Swarup Paul ◽  
Soudip Hazra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Engine Performance ◽  
Wear Particle ◽  
Severity Index ◽  
Ann Model ◽  
Artificial Neural ◽  
Engine Failure ◽  
Artificial Neural Network Ann ◽  
Nonlinear Autoregressive

Abstract This paper reflects on the use of the Artificial Neural Network ( ANN) approach to diagnose and interpret engine failure behaviour. The current research focuses on the analysis of quantitative wear trend patterns through Condition Tracking (CM) and soft computational approaches. Oil analysis has been carried out to observe the engine failure trend. An ANN model using a Nonlinear Autoregressive with Exogenous Input (NARX) architecture has been employed to predict quantitative outputs such as Wear Particle Concentration (WPC), Wear Severity Index (WSI), Severity Index (SI) and Percentage of Large Particle (PLP) in connection with input functions of Engine Running Hours, RPM and oil temperature. Correlation function and error similarity are statistically evaluated to represent the model's robustness and effectively chart the loss input-output sequence. The subsequent ANN model demonstrates the capabilities for advance diagnosis and better prediction of engine performance.

scholarly journals Data Mining using Artificial Neural Network POS_NEG Rules

2013 ◽  
Vol 11 (6) ◽  
pp. 2709-2714
Author(s):  
Pushkar Shinde ◽  
Dr. Varsha Patil
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Sensitivity Method

Diabetes patients are increasing in number so it is necessary to predict , treat and diagnose the disease. Data Mining can help to provide knowledge about this disease. The knowledge extracted using Data Mining can help in treating and preventing the disease. Artificial Neural Network (ANN) can be used to create an classifier from the data. The neural network is trained using backpropagation algorithm The knowledge stored in the neural network is used to predict the disease. The knowledge stored in neural network is extracted using Pos-Neg sensitivity method. The knowledge extracted is in form of sensitivity analysis to analyze the disease and in turn help in treating the disease.

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