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Neural Network Approach to Estimate the Performance of Processes Involved in Vehicle Service
Technical Paper
2015-26-0043
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Regular service of the vehicle is to be done with high precision service equipment, to ensure the factory performance of the vehicle over the entire life of product usage. However, complex nature of the physical processes involved in the service of the vehicle subsystems makes it costly for optimizing the service equipment performance for entire range of operation. Air-conditioning service (ACS) equipment is one such product in the diagnostics domain which deals with compressible, transient and two phase flow in open loop systems.
Development of control system for the service equipment to perform optimally over the entire operational range requires accurate mathematical model of the system under study. Application of mathematical model based approach requires calculation of geometrical details, environment information and fluid properties during the process for estimating the process behavior. Generating these empirical details may require intensive testing with usage of advanced instrumentation which makes this approach more complex, time consuming and costly. And this may not be feasible in the available time to market the design solution.
Neural Network (NN) can be applied for modeling complex nonlinear systems that are not easily modeled with a closed-form equation. This approach requires correct input-output model of the process which specifies the right input and output parameters along with the environmental parameters over the entire range of operation. It doesn't require accurate intermediate details of the process.
This paper presents a practitioners approach for applying a multilayer Neural Network based model with back propagation algorithm for optimization of processes involved in vehicle service. Refrigerant recharge process in ACS equipment is taken as a case study to explain the approach. Statistical technique (Design of Experiments) is used for minimizing the test data points which are required for training the NN. As a result, optimized weightage of the affecting parameters is obtained. This NN model can be used to estimate the performance of the physical process by entering the required operating set as input values.
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Citation
Kashyap, R., Sunkari, V., and Verma, P., "Neural Network Approach to Estimate the Performance of Processes Involved in Vehicle Service," SAE Technical Paper 2015-26-0043, 2015, https://doi.org/10.4271/2015-26-0043.Also In
References
- Rajesh k. , Santhosh k. and Venkatesh GK. Technological challenges in servicing of Mobile Air Conditioners (MAC) and overcoming methods SAE Technical Paper 2013-01-2878 2013 10.4271/2013-01-2878
- Thomas M. and Thomas P. A Neural Network Approach to Estimating Material Properties Corporate Research and Development Siemens AG
- SAE International surface vehicle standard HFC-134a(R-134a) Recovery/Recycling Equipment and Recovery/Recycling/Recharging for Mobile Air-Conditioning Systems SAE standard J2788 Dec 2006
- SAE International surface vehicle standard Standard of Purity for Recycled HFC-134a(R134a) for Use in Mobile Air-Conditioning Systems SAE standard J2099 Feb 1999
- Goh A.T.C. Back-propagation neural networks for modeling complex systems Elsevier J. for Artificial Intelligence in Engineering 0954-1810 94 00011 5 1995
- Dimitris C. and Lyle H. A Hybrid Neural Network-First Principles Approach to Process Modeling AlChE Journal 37 10 Oct 1992
- Ganesan N. , Venkatesh K. and Rama M.A. Application of Neural Network in Diagnosing Cancer Disease Using Demographic Data Int. J. of comp. applications, 0975-8887 1 26
- www.scilab.org/content/download/247/1702/file/introscilab.pdf