Diesel engines diagnosis through analysis of lubricating oil

Expert systems can be applied to solve complex problems in different areas of knowledge. It have been used by specialists to help diagnose problems in areas such as medicine and engineering. More recently techniques from computational intelligence area, such as neural networks and fuzzy logic were used to build expert systems. These systems can learn from examples provided by problems solved in the past. The success of the expert system depends on how good the model is defined. This paper presents the modeling of an expert system used to classify the internal combustion diesel about the level of corrosion of its metal parts on a three-level scale. The system bases its decision on the results of used lubricating oil analysis. It uses a neuro-fuzzy model, called ANFIS, trained from past diagnoses performed by specialists. The paper discusses how important parameters of the system were defined, such as which variables obtained from the analysis of the oil should be used, the number of training epochs and the number of membership functions used to map the inputs.