Nonlinear parity equation based residual generation for diagnosis of automotive engine faults

The parity equation residual generation method is a model-based fault detection and isolation scheme that has been applied with some success to the problem of monitoring the health of engineering systems. However, this scheme fails when applied to significantly nonlinear systems. This paper presents the application of a nonlinear parity equation residual generation scheme that uses forward and inverse dynamic models of nonlinear systems, to the problem of diagnosing sensor and actuator faults in an internal combustion engine, during execution of the United States Environmental Protection Agency Inspection and Maintenance 240 driving cycle. The Nonlinear AutoRegressive Moving Average Model with eXogenous inputs technique is used to identify the engine models required for residual generation. The proposed diagnostic scheme is validated experimentally and is shown to be sensitive to a number of input and sensor faults while remaining robust to the unmeasured load torque disturbance.