The Application of Fuzzy Logic to the Diagnosis of Automotive Systems 970208

The evolution of the diagnostic equipment for automotive application is the direct effect of the implementation of sophisticated and high technology control systems in the new generation of passenger cars.
One of the most challenging issues in automotive diagnostics is the ability to assess, to analyze, and to integrate all the information and data supplied by the vehicle's on-board computer. The data available might be in the form of fault codes or sensors and actuators voltages. Moreover, as environmental regulations get more stringent, knowledge of the concentration of different species emitted from the tailpipe during the inspection and maintenance programs can become of great importance for an integrated powertrain diagnostic system.
A knowledge-based diagnostic tool is one of the approaches that can be adopted to carry out the challenging task of detecting and diagnosing faults related to the emissions control system in an automobile. In particular, the application of fuzzy logic to the diagnosis of automotive systems provides a novel approach that can integrate all of the information available about the automotive systems in a diagnostic frame.
In this paper the use of fuzzy rules generated from experimental work, and the experience of engineers and technicians in the automotive field is used to design an integrated powertrain diagnostic system. In this context, experimental results of the work done on the integration of fault codes, mathematical model-based approaches, and exhaust emission levels are provided.


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