scholarly journals Application of Artificial Neural Network for Internal Combustion Engines: A State of the Art Review

2021 ◽  
Author(s):  
Aditya Narayan Bhatt ◽  
Nitin Shrivastava
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Internal Combustion Engines ◽  
State Of The Art ◽  
Internal Combustion ◽  
Combustion Engines ◽  
Artificial Neural

scholarly journals Valve Fault Diagnosis in Internal Combustion Engines Using Acoustic Emission and Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
S. M. Jafari ◽  
H. Mehdigholi ◽  
M. Behzad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Internal Combustion Engines ◽  
Signal Amplitude ◽  
Time Domain Analysis ◽  
Internal Combustion ◽  
Combustion Engines ◽  
Valve Leakage ◽  
Artificial Neural

This paper presents the potential of acoustic emission (AE) technique to detect valve damage in internal combustion engines. The cylinder head of a spark-ignited engine was used as the experimental setup. The effect of three types of valve damage (clearance, semicrack, and notch) on valve leakage was investigated. The experimental results showed that AE is an effective method to detect damage and the type of damage in valves in both of the time and frequency domains. An artificial neural network was trained based on time domain analysis using AE parametric features (AErms, count, absolute AE energy, maximum signal amplitude, and average signal level). The network consisted of five, six, and five nodes in the input, hidden, and output layers, respectively. The results of the trained system showed that the AE technique could be used to identify the type of damage and its location.

scholarly journals State-of-the-art in artificial neural network applications: A survey

Heliyon ◽  
2018 ◽  
Vol 4 (11) ◽  
pp. e00938 ◽  
Author(s):  
Oludare Isaac Abiodun ◽  
Aman Jantan ◽  
Abiodun Esther Omolara ◽  
Kemi Victoria Dada ◽  
Nachaat AbdElatif Mohamed ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
State Of The Art ◽  
Network Applications ◽  
Artificial Neural ◽  
Neural Network Applications

scholarly journals Improved non-linear transfer function and neural network methods of flow routing for real-time forecasting

2001 ◽  
Vol 3 (3) ◽  
pp. 153-164 ◽  
Author(s):  
D. F. Lekkas ◽  
C. E. Imrie ◽  
M. J. Lees
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Real Time ◽  
State Of The Art ◽  
Comparison Results ◽  
Network Methods ◽  
Forecasting Performance ◽  
Non Linear ◽  
Artificial Neural

Data-based methods of flow forecasting are becoming increasingly popular due to their rapid development times, minimum information requirements, and ease of real-time implementation, with transfer function and artificial neural network methods the most commonly applied methods in practice. There is much antagonism between advocates of these two approaches that is fuelled by comparison studies where a state-of-the-art example of one method is unfairly compared with an out-of-date variant of the other technique. This paper presents state-of-the-art variants of these competing methods, non-linear transfer functions and modified recurrent cascade-correlation artificial neural networks, and objectively compares their forecasting performance using a case study based on the UK River Trent. Two methods of real-time error-based updating applicable to both the transfer function and artificial neural network methods are also presented. Comparison results reveal that both methods perform equally well in this case, and that the use of an updating technique can improve forecasting performance considerably, particularly if the forecast model is poor.

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