Automatic sensor assignment of a supermarket refrigeration system

2010 IEEE International Symposium on Industrial Electronics

Ravn

Izadi‐Zamanabadi

et al. 2010

Self Cite

In active diagnosis the system is excited by a signal that aims to uncover latent errors. However, the diagnosis signal may destabilize the system, in particular in an openloop structure, but also in a closed-loop structure, because the nominal controller is designed to stabilize the nominal system. This paper presents a method for active diagnosis of Mixed Logical Dynamical (MLD) systems where instability is avoided: The diagnoser looks for steady states of both the normal and faulty system which are reachable by the same input such that the corresponding outputs are distinguishable from each other. The input is applied to the system and the condition of the system is determined based on the output. Thus this excitation preserves stability. The method can be useful in a design phase to find a sensor allocation which guarantees diagnosability. The method is tested on the two tank benchmark example.

Section: Plant Y Umentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Active diagnosis of MLD systems using distinguishable steady outputs

2010 IEEE International Symposium on Industrial Electronics

Ravn

Izadi‐Zamanabadi

et al. 2010

Self Cite

“…In our previous work [18], we proposed an active fault diagnosis method for linear hybrid systems in discrete time based on reach set computation for faulty and normal systems. The results are extended to automatic sensor assignment in [17]. Computation of reachable states is computationally burdensome, especially for a hybrid system.…”

Section: Introductionmentioning

confidence: 99%

Active diagnosis of hybrid systems — A model predictive approach

2009 IEEE International Conference on Control and Automation

Ravn

Izadi-zamabadi

et al. 2009

Self Cite

Abstract-A method for active diagnosis of hybrid systems is proposed. The main idea is to predict the future output of both normal and faulty model of the system; then at each time step an optimization problem is solved with the objective of maximizing the difference between the predicted normal and faulty outputs constrained by tolerable performance requirements. As in standard model predictive control, the first element of the optimal input is applied to the system and the whole procedure is repeated until the fault is detected by a passive diagnoser. It is demonstrated how the generated excitation signal can be used as a test signal for sanity check at the commissioning or for detection of faults hidden by regulatory actions of the controller. The method is tested on the two tank benchmark example.

“…In Tabatabaeipour, Ravn, Izadi-Zamanabadi, and Bak (2009a), an active fault diagnosis method for linear hybrid systems in discrete time based on reach set computation for faulty and normal systems is proposed. The results are extended to automatic sensor assignment in Tabatabaeipour, Izadi-Zamanabadi, Bak, and Ravn (2009b). The problem is reformulated in Tabatabaeipour (2010) as a mixed integer optimisation problem for active diagnosis of a hybrid system using the mixed logical dynamical framework.…”

Section: Introductionmentioning

confidence: 99%

Active fault detection and isolation of discrete-time linear time-varying systems: a set-membership approach

International Journal of Systems Science

2013

Link back to DTU OrbitCitation (APA): Tabatabaeipour, M. (2013). Active fault detection and isolation of discrete-time linear time-varying systems: a set-membership approach. International Journal of Systems Science. DOI: 10.1080DOI: 10. /00207721.2013 This article was downloaded by: [ PLEASE SCROLL DOWN FOR ARTICLETaylor & Francis makes every effort to ensure the accuracy of all the information (the "Content") contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions Active fault detection and isolation (AFDI) is used for detection and isolation of faults that are hidden in the normal operation because of a low excitation signal or due to the regulatory actions of the controller. In this paper, a new AFDI method based on set-membership approaches is proposed. In set-membership approaches, instead of a point-wise estimation of the states, a set-valued estimation of them is computed. If this set becomes empty the given model of the system is not consistent with the measurements. Therefore, the model is falsified. When more than one model of the system remains un-falsified, the AFDI method is used to generate an auxiliary signal that is injected into the system for detection and isolation of faults that remain otherwise hidden or non-isolated using passive FDI (PFDI) methods. Having the set-valued estimation of the states for each model, the proposed AFDI method finds an optimal input signal that guarantees FDI in a finite time horizon. The input signal is updated at each iteration in a decreasing receding horizon manner based on the set-valued estimation of the current states and un-falsified models at the current sample time. The problem is solved by a number of linear and quadratic programming problems, which result in a computationally efficient algorithm. The method is tested on a numerical example as well as on the pitch actuator of a benchmark wind turbine.