Virtual Sensors for Automotive Engine Sensors Fault Diagnosis in Second-Order Sliding Modes

Automotive engine functions are entirely dependent on installed sensors performance. Any malfunction in the sensors can lead to degraded engine efficiency. This manuscript presents a novel scheme to devise virtual sensors for health monitoring of engine air intake path sensors. The proposed scheme assists in: Sensing of critical immeasurable parameters like Volumetric and Combustion efficiency, development of Virtual Sensor from manifold pressure dynamics for rotational speed and vice versa, Health Monitoring of the manifold pressure and crankshaft sensor. For the suggested scheme, two robust second-order sliding mode observers are employed that require two state mean value engine model based on inlet manifold pressure and rotational speed dynamics. The proposed methodology has the potential of online implementation on any automotive engine after minor tuning. In this paper, the procedure is customized for 1.3 L gasoline engine sensors: Manifold Pressure and Crankshaft sensor. The implementation results demonstrate that all the three mentioned tasks are accomplished efficiently.

[1]  S. Singh,et al.  Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems , 2007, IEEE Transactions on Instrumentation and Measurement.

[2]  Giorgio Rizzoni,et al.  Detection of sensor failures in automotive engines , 1991 .

[3]  K. B. Goh,et al.  Fault diagnostics using sliding mode techniques , 2002 .

[4]  Arie Levant,et al.  Higher-order sliding modes, differentiation and output-feedback control , 2003 .

[5]  Alexander S. Poznyak,et al.  A New Robust Sliding-Mode Observer Design for Monitoring in Chemical Reactors , 2004 .

[6]  Willard W. Pulkrabek,et al.  Engineering Fundamentals of the Internal Combustion Engine , 1997 .

[7]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[8]  Neil Genzlinger A. and Q , 2006 .

[9]  Antonella Ferrara,et al.  Fault Detection for Robot Manipulators via Second-Order Sliding Modes , 2008, IEEE Transactions on Industrial Electronics.

[10]  Yuri B. Shtessel,et al.  Higher order sliding modes , 2008 .

[11]  Antonio Pietrosanto,et al.  Analytical redundancy for sensor fault isolation and accommodation in public transportation vehicles , 2003, Proceedings of the 20th IEEE Instrumentation Technology Conference (Cat. No.03CH37412).

[12]  Uwe Kiencke,et al.  Automotive Control Systems: For Engine, Driveline, and Vehicle , 2000 .

[13]  Antonio Pietrosanto,et al.  On-line sensor fault detection, isolation, and accommodation in automotive engines , 2002, IMTC/2002. Proceedings of the 19th IEEE Instrumentation and Measurement Technology Conference (IEEE Cat. No.00CH37276).

[14]  Qing Wu,et al.  Robust Fault Diagnosis of a Satellite System Using a Learning Strategy and Second Order Sliding Mode Observer , 2010, IEEE Systems Journal.

[15]  Derek Caveney,et al.  Cooperative Vehicular Safety Applications , 2010, IEEE Control Systems.

[16]  Mattias Nyberg,et al.  Model Based Diagnosis of Both Sensor-Faults and Leakage in the Air-Intake System of an SI-Engine , 1999 .

[17]  Lino Guzzella,et al.  Introduction to Modeling and Control of Internal Combustion Engine Systems , 2004 .

[18]  D. Soffker,et al.  Virtual Sensors for Diagnosis and Prognosis Purposes in the Context of Elastic Mechanical Structures , 2009, IEEE Sensors Journal.

[19]  M. Staroswiecki,et al.  Fault estimation in nonlinear uncertain systems using robust/sliding-mode observers , 2004 .

[20]  L. Fridman,et al.  Second order sliding mode observer for estimation of velocities, wheel sleep, radius and stiffness , 2006, 2006 American Control Conference.

[21]  Hong Guo,et al.  Automotive signal fault diagnostics - part I: signal fault analysis, signal segmentation, feature extraction and quasi-optimal feature selection , 2003, IEEE Trans. Veh. Technol..

[22]  Pau-Lo Hsu,et al.  Diagnosis of multiple sensor and actuator failures in automotive engines , 1995 .

[23]  B. Castillo-Toledo,et al.  Model-Based Fault Diagnosis Using Sliding Mode Observers to Takagi-Sugeno Fuzzy Model , 2005, Proceedings of the 2005 IEEE International Symposium on, Mediterrean Conference on Control and Automation Intelligent Control, 2005..

[24]  S. Kumar,et al.  Development of ANN-based virtual fault detector for Wheatstone bridge-oriented transducers , 2005, IEEE Sensors Journal.

[25]  J. P. R. Jongeneel,et al.  Input redundant internal combustion engine with linear quadratic Gaussian control and dynamic control allocation , 2009 .

[26]  Qudrat Khan,et al.  Robust Parameter Estimation of Nonlinear Systems Using Sliding-Mode Differentiator Observer , 2011, IEEE Transactions on Industrial Electronics.

[27]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[28]  Christopher Edwards,et al.  Sliding mode observers for detection and reconstruction of sensor faults , 2002, Autom..

[29]  E. Balaban,et al.  Modeling, Detection, and Disambiguation of Sensor Faults for Aerospace Applications , 2009, IEEE Sensors Journal.

[30]  Christopher Edwards,et al.  Sensor fault tolerant control using sliding mode observers , 2006 .

[31]  Mehrdad Saif,et al.  High Order Sliding Mode Observers and Differentiators-Application to Fault Diagnosis Problem , 2008 .