Fuzzy Logic Control Based Failure Detection and Identification (FDI) Module for Internal Combustion (IC) Engines

In this study, an adaptive, supervisory, and hybrid fuzzy controllers as well as a fuzzy estimator are developed based on experimental and simulation data and expert's knowledge of the normal working of diagnostic systems.

The advantage of the developed control system is that it has fewer parameters to tune; it also makes the system adaptable for changing environments or malfunctions; and provides the supervision of the overall performance of the engine management system. For the fuzzy estimator part of the system we employ the least square and gradient methods for the premise and consequent of the rule. This study introduces a new design method for FDI module. It also provides an efficient and easy to implement control system that can be incorporated into existing module with minimum changes, adjustment, and cost. The results of the simulated studies of a variety of failure scenarios shows that the proposed control module is capable of accurately identifying failures and malfunctions in addition to providing diagnoses and corrective actions for different failure modes. The comparison of our results with other related results show good agreement in response and accuracy in the identifications and diagnosis. The simulated results are then contrasted with experimental results obtained under the same conditions and induced failures scenarios. Our proposed method shows a good agreement with experiments in predictions, identifications, and diagnosis for variety of failure modes or malfunctions.