Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-26T10:13:49.417Z Has data issue: false hasContentIssue false
  • English
  • Français

An expert system approach to design of automotive air-conditioning systems

Published online by Cambridge University Press:  27 February 2009

Atul Bajpai
Atul Bajpai
Affiliation:
Artificial Intelligence, Advanced Engineering, General Motors Corporation, Warren, MI 48090
Get access Rights & Permissions [Opens in a new window]

Abstract

An expert system approach for designing air-conditioning systems for cars and trucks is presented. A brief introduction to the automotive application of the vapor-compression refrigeration cycle is provided as general background. The method presented uses an integrated approach combining the power of conventional analysis programs, databases and model-based expert system technology. Some sample rules from the knowledge base have been included in the paper to illustrate the application of the domain knowledge and its interaction with algorithmic programs. The system architecture is very open and modular, and it lends itself to easy modifications and future expansions. Possibilities for system enhancements are also outlined in the paper. The approach presented in this paper provides substantial benefits to the automotive air-conditioning design engineer particularly in the early stages of new vehicle platform planning and development. A pilot system has been successfully tested at General Motors for preliminary design of automotive air-conditioning systems.

Keywords

expert systemair-conditioningdesign
Type
Research Article
Information
AI EDAM , Volume 8 , Issue 1 , Winter 1994 , pp. 1 - 11
Copyright
Copyright © Cambridge University Press 1994

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Bajpai, A. (1988). An expert system model for general purpose diagnostics of manufacturing equipment, Manufacturing Review, ASME J. 1(3), 180–187.Google Scholar
Bajpai, A., & Marczewski, R.W. (1989). CHARLEY: An expert system for diagnostics of manufacturing equipment, Proc. 1st Innovative Applications of Artificial Intelligence Conference, AAAI, Stanford University, Palo Alto, CA.Google Scholar
Bajpai, A., & Sanders, B.A. (1990). Artificial intelligence for automobile manufacturing. In Encyclopedia of Computer Science and Technology, Vol 22, pp. 67–83. Marcel Dekker, New York.Google Scholar
Brothers, P.W. (1988). Knowledge engineering for HVAC expert systems, ASHRAE Trans. 94(1), 1063–1073.Google Scholar
Brothers, P.W., & Cooney, K.P. (1989). A knowledge-based system for comfort diagnostics, ASHRAE J. 31(9), 60–67.Google Scholar
Brown, D.C., & Chandrasekaran, B. (1985). Expert systems for a class of mechanical design activity. In Knowledge Engineering in Computer-Aided Design (Gero, J., Ed.), pp. 259–260. North-Holland, Amsterdam.Google Scholar
Brown, D.C., & Chandrasekaran, B. (1986). Knowledge and control for a mechanical design expert system, Computer (July), 92–100.CrossRefGoogle Scholar
Camejo, P.J., & Hittle, D.C. (1989). An expert system for the design of heating, ventilating and air-conditioning systems, ASHRAE Trans. 95(1), 379–386.Google Scholar
Culp, C.H. (1989). Expert systems in preventive maintenance and diagnostics, ASHRAE J. 31(7), 24–27.Google Scholar
Culp, C.H., Haberl, J., Norford, L., Brothers, P.W., & Hall, J.D. (1990). The impact of AI technology within the HVAC industry, ASHRAE J. (December), 12–22.Google Scholar
Doyle, J. (1979). A truth maintenance system, Artificial Intelligence 12, 231–272.CrossRefGoogle Scholar
Friel, P.G., Mayer, R.J., Lockledge, J.C., Smith, G.M., & Shulze, R.C. (1989). Coolsys: a cooling systems design assistant. In Innovative Applications of Artificial Intelligence, (Schorr, H. and Rappaport, A., Eds.), pp. 203–212. Cambridge, MA, The MIT Press.Google Scholar
Hall, J.D., & Deringer, J.J. (1989). Computer software invades the HVAC market, ASHRAE J. 31, 32–44.Google Scholar
Jafar, M., Bahill, A.T., & Osborn, D.E. (1991). A knowledge-based system for residential HVAC applications, ASHRAE J. (January), 20–26.Google Scholar
Kaler, G. (1990). Embedded expert system development for monitoring packaged HVAC equipment, ASHRAE Trans. 96(2), 733–742.Google Scholar
Kitzmiller, C.T. and Kowalik, J.S. (1987). Coupling symbolic and numeric computing in knowledge-based systems, AI Magazine 8(2), 85–90.Google Scholar
Mittal, S., Dym, C. and Morjaria, M. (1985). PRIDE: an expert system for design of paper handling systems. In Application of Knowledge-Based Systems to Engineering Analysis and Design, (Dym, C., Ed.), pp. 99–116. New York: ASME.Google Scholar
Ruth, D.W. (1975). Simulation modeling of automobile comfort cooling requirements, ASHRAE J. 17(5), 53–55.Google Scholar
Sriram, D., Stephanopoulos, G., Logcher, R., Gossard, D., Groleau, N., Serrano, D., & Navinchandrá, D. (1989). Knowledge-based system applications in engineering design: Research at MIT, AI Magazine 10(3), 79–96.Google Scholar
Steele, R.L., Richardson, S.A., & Winchell, M.A. (1989). Design-Advisor: a knowledge-based integrated circuit design critic. In Innovative Applications of Artificial Intelligence, (Schorr, H. and Rappaport, A., Eds.), pp. 213–224. Cambridge, MA: The MIT Press.Google Scholar
Stoecker, W.F., & Jones, J.W. (1986). Refrigeration and Air Conditioning. New York: McGraw-Hill.Google Scholar