Misfire Fault Detection In Spark Ignition Engine Using Hybrid Model
Automotive industry has added the self-diagnostic features in vehicles to improve the reliability of vehicle. Research is being carried out to predict the faults that are going to occur in near future by the analysis of current values of vehicle variables. The presented work stressed on the application of Markov chains for the early detection of misfire fault in spark ignition engines. To define the states of Markov chains a novel hybrid model is presented to represent SI engine under steady state conditions.
A survey of existing mathematical models of SI engine is provided. The hybrid model of SI engine was not widely studied area in the past. The proposed hybrid model with both continuous and discrete states is described in details. The basic assumption of modeling is that the cylinder contributing engine power is the basic active sub-component that provides power for useful work as well as to other cylinders that need power for compression, suction or exhaust. The cylinder providing power is considered as the active cylinder. The active cylinder is switched periodically in a cyclic manner.
The continuous states of hybrid model are defined by considering each cylinder of SI engine as the sub-systems of hybrid model. The switching of active cylinder is considered as discrete state of hybrid model. The model is simulated to study the crankshaft speed fluctuations observed in SI engine. The simulation results are then verified experimentally on 1300 cc engine of a production vehicle from Honda by acquiring data using Data Acquisition Cards of National Instrument Inc. The properties of presented model are then studied and some results are established for onward stochastic analysis.
The crankshaft speed fluctuation signal is analyzed using the properties of the proposed model and it is established that the peak values of observed speed during an ignition cycle is Gaussian and Markov. The peak value of crankshaft speed observed in each ignition cycle is associated with one of the cylinders or sub-systems. In this way four possible states are identified where ith state correspond to the peak value of crankshaft speed associated with ith sub-system of hybrid model. It is assumed that all states are equally probable when engine is healthy and that the fault would bias one of the states. The proposed novel fault detection algorithm identifies the biasing of a state by the calculation of Limiting State Probability of Markov Chains to indicate the fault.
The data for both healthy and faulty engine condition is generated using hybrid model and analyzed using proposed fault detection method. The algorithm is finally verified experimentally by acquiring data from SI engine both under no fault condition and faulty condition and analyzing it for the existence of fault.
The correctness of fault predicted by algorithm is mathematically analyzed using analysis similar to ROC analysis. In error analysis the fault is predicted using proposed algorithm and compared with the data observed experimentally to study the false positive events. The plot of analysis demonstrates the affectivity of algorithm.