Hybrid System Model Based Fault Diagnosis of Automotive Engines

  • E. P. Nadeer
  • S. Mukhopadhyay
  • A. Patra


This chapter presents an online fault diagnosis scheme for a spark ignition automotive engine, based on a complex physics-based Hybrid System Model (HSM). The overall diagnostic signal processing scheme starts with a continuous state estimation stage comprising a single Extended Kalman Filter (EKF) estimator that employs a nominal normal (i.e. without fault) HSM of the engine in which the nonlinear continuous dynamical model is switched according to the discrete state transition of the HSM. This is followed by a residual prediction stage, and a fault detection and isolation stage. The nonlinear physics-based HSM is such that, its overall continuous state vector comprises elements which have physical interpretation (e.g. temperature, mass fraction, etc.), is an augmentation of state sub-vectors for each component or reservoir of the engine system. Since the variables have physical origin, the transitions between discrete modes of the HSM do not cause any discrete jump in the continuous state vector. Further, the augmentation of component state sub-vectors, elements of which are derived from related thermodynamics, makes it easy to derive the analytical expressions related to the Jacobian matrices needed for EKF. The covariance matrices of the process and measurement noise are also approximately computed online. This ensures that the nominal estimator switches to the correct discrete mode quickly in response to the changes in the plant. Engine faults, such as a leak in the manifolds, injector block, cylinder valve wear, and sensor failures can be captured easily by suitable parameterization of the model. Use of such physics-based parametric models, and reuse of the Jacobian matrices from the nominal estimator, greatly reduce the real-time computational requirements. Using this technique, in the residual prediction stage, the residuals from the nominal estimator under each fault scenario could be predicted for unit magnitude of the faults. In the last stage, a Generalized Likelihood Ratio Test (GLRT) is applied on the predicted residual for each fault and the actual residual from the nominal estimator, to detect and isolate the faults. Propositional logic is used to isolate the fault when multiple fault detection functions are triggered while using the GLRT method. The method is demonstrated on realistic simulations.


Spark ignition engines State space modelling Hybrid dynamical systems Model based fault diagnosis Adaptive state estimation Extended Kalman filter Unscented Kalman filter Particle filters Covariance estimation Estimator residual Fault detection Fault isolation Fault incidence matrix Intake manifold leak Exhaust manifold leak Cylinder valve faults Sensor bias faults Generalized likelihood ratio test 


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • E. P. Nadeer
    • 1
    • 2
  • S. Mukhopadhyay
    • 2
  • A. Patra
    • 2
  1. 1.KPIT TechnologiesPuneIndia
  2. 2.Department of Electrical EngineeringIndian Institute of Technology KharagpurKharagpurIndia

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