Robust fault detection using consistency techniques for uncertainty handling

Often practical performance of analytical redundancy for fault detection and diagnosis is decreased by uncertainties prevailing not only in the system model, but also in the measurements. In this paper, the problem of fault detection is stated as a constraint satisfaction problem over continuous domains with a big number of variables and constraints. This problem can be solved using modal interval analysis and consistency techniques. Consistency techniques are then shown to be particularly efficient to check the consistency of the analytical redundancy relations (ARRs), dealing with uncertain measurements and parameters. Through the work presented in this paper, it can be observed that consistency techniques can be used to increase the performance of a robust fault detection tool, which is based on interval arithmetic. The proposed method is illustrated using a nonlinear dynamic model of a hydraulic syste

Model-based fault diagnosis for aerospace systems: a survey

http://pig.sagepub.com/content/early/2012/01/06/0954410011421717International audienceThis survey of model-based fault diagnosis focuses on those methods that are applicable to aerospace systems. To highlight the characteristics of aerospace models, generic nonlinear dynamical modeling from flight mechanics is recalled and a unifying representation of sensor and actuator faults is presented. An extensive bibliographical review supports a description of the key points of fault detection methods that rely on analytical redundancy. The approaches that best suit the constraints of the field are emphasized and recommendations for future developments in in-flight fault diagnosis are provided

Fault diagnosis of a wind farm using interval parity equations

Trabajo presentado al 19th IFAC World Congress celebrado del 24 al 29 de agosto de 2014 en Cape Town (Sudafrica).In this paper, the problem of fault diagnosis of a wind farm is addressed using interval parity equations. Fault detection is based on the use of parity equations and unknown but bounded description of the noise and modeling errors. The fault detection test is based on checking the consistency between the measurements and the model by finding if the formers are inside the interval prediction bounds. The fault isolation algorithm is based on analyzing the observed fault signatures on-line, and matching them with the theoretical ones obtained using structural analysis. Finally, the proposed approach is tested using the wind farm benchmark proposed in the context of the wind farm FDI/FTC competition.This work has been funded by the Spanish MINECO through the project CYCYT SHERECS (ref. DPI2011-26243), by the European Commission through contract i-Sense (ref. FP7-ICT-2009-6-270428) and by AGAUR through the contract FI-DGR 2013 (ref. 2013FIB00218).Peer Reviewe

Fault detection and isolation in critical infrastructure systems

Critical infrastructure systems (CIS) are complex large-scale systems which in turn require highly sophisticated supervisory control systems to ensure that high performance can be achieved and maintained under adverse conditions. The global CIS Real-Time Control (RTC) need of operating in adverse conditions involves, with a high probability, sensor and actuator malfunctions (faults). This problem calls for the use of an on-line Fault Detection and Isolation (FDI) system able to detect such faults. This paper proposes a FDI mechanism that extends the classical Boolean fault signature matrix concept taking into account several fault signal properties to isolate faults in CIS. To exemplify the proposed FDI scheme in CIS, the Barcelona drinking water network is used as a case study.Preprin