Sensor fault masking of a ship propulsion system

Abstract This paper presents the results of a study on fault-tolerant control of a ship propulsion benchmark (Izadi-Zamanabadi and Blanke, 1999), which uses estimated or virtual measurements as feedback variables. The estimator operates on a self-adjustable design model so that its outputs can be made immune to the effects of a specific set of component and sensor faults. The adequacy of sensor redundancy is measured using the control reconfigurability (Wu, Zhou, and Salomon, 2000), and the number of sensor based measurements are increased when this level is found inadequate. As a result, sensor faults that are captured in the estimator's design model can be tolerated without the need for any reconfiguration actions. Simulations for the ship propulsion benchmark show that, with additional sensors added as described, satisfactory fault-tolerance is achieved under two additive sensor faults, an incipient fault, and a parametric fault, without having to alter the original controller in the benchmark.

[1]  Christopher Edwards,et al.  A sliding mode observer based FDI scheme for the ship benchmark , 1999, 1999 European Control Conference (ECC).

[2]  M. V. Iordache,et al.  Diagnosis and Fault-Tolerant Control , 2007, IEEE Transactions on Automatic Control.

[3]  Mogens Blanke,et al.  FAULT MONITORING AND RE-CONFIGURABLE CONTROL FOR A SHIP PROPULSION PLANT , 1998 .

[4]  E. C. Kerrigan,et al.  Fault-tolerant control of a ship propulsion system using model predictive control , 1999, 1999 European Control Conference (ECC).

[5]  Youmin Zhang,et al.  Fault Diagnosis for A Ship Propulsion Benchmark: Part I , 1999 .

[6]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .

[7]  N. Eva Wu,et al.  Control reconfigurability of linear time-invariant systems , 2000, Autom..

[8]  T. F. Lootsma,et al.  Observer-based Fault Detection and Isolation for Nonlinear Systems , 2001 .

[9]  N. Eva Wu,et al.  Coverage in fault-tolerant control , 2004, Autom..

[10]  M. Blanke,et al.  Adaptive observer for diesel fault detection in ship propulsion benchmark , 1999, 1999 European Control Conference (ECC).

[11]  Youmin Zhang,et al.  Detection, estimation, and accommodation of loss of control effectiveness , 2000 .

[12]  Vincent Cocquempot,et al.  Ship Propulsion Control and Reconfiguration , 2001 .

[13]  Mogens Blanke,et al.  Cheap diagnosis using structural modelling and fuzzy-logic-based detection , 2003 .

[14]  Karl Johan Åström,et al.  Control of complex systems , 2001 .

[15]  Mogens Blanke,et al.  A Ship Propulsion System as a Benchmark for Fault-tolerant Control , 1997 .

[16]  B. Moore.  Principal component analysis in linear systems: Controllability, observability, and model reduction , 1981 .

[17]  Ron J. Patton,et al.  Fault-Tolerant Control: The 1997 Situation , 1997 .

[18]  G. L. Gissinger,et al.  Benchmark application of fuzzy observers to fault detection on a ship propulsion system , 1999, 1999 European Control Conference (ECC).

[19]  Roozbeh Izadi-Zamanabadi.  Fault-tolerant Supervisory Control: System Analysis and Logic Design , 1999 .

[20]  G. Schreier,et al.  Fault tolerant ship propulsion control: Sensor fault detection using a nonlinear observer , 1999, 1999 European Control Conference (ECC).

[21]  Lorenzo Marconi,et al.  Fault-tolerant control of the ship propulsion system benchmark , 2003 .