Optimal Input Design for Subspace-Based Fault Detection and Identification

This study focuses on input design for subspace based fault detection and identification methods and investigates its possible advantages over using noise inputs. In several real applications the noise available in environment is the only input to the system and in some cases produce low quality output data for subspace identification and fault detection purposes. Therefore, model order may be underestimated. Due to the nature of subspace based methods, some modes of the system may not appear in the response as the input is not strong enough to excite these modes. In order to improve the result, a method is suggested in literature, that is to use "rotated" input. The rotated input design is proposed in several papers to apply to "ill conditioned systems" in which the vector of different outputs are typically close to collinearity if a white noise is used. In this report, we use this technique to verify possible improvement of subspace-based identification method including output-only, and input-output approaches. Then, for the first time we investigate the possible impacts of the rotated input on subspace base fault detection method. Simulations on a high-purity distillation column shows that this auxiliary input can improve subspace-based fault detection and identification

Application of Real-Time PCR method for evaluation of measles vaccine heat stability

     The Plaque Forming Unit(PFU) and Tissue Culture Infectious Dose50(TCID50) methods are used for evaluation of vaccine heat stability and effect of various stabilizers on thermal stability of vaccines. The aim of present study is using Real-Time PCRtechniquefor estimation of vaccine degradation rate and thermal stability of measles vaccines. Lyophilized measles vaccines containing three various stabilizers were reconstituted with distilled water. Three vial of each vaccine incubated at25˚C for 0, 4 and 8 hours. Titer of virus in vaccines calculated by TCID50 method. Also after RNA Extraction and cDNA synthesis, the RNA copy numbers of viruses in vaccines were estimated by absolute quantitative Real-Time PCRtesting. The data were analyzedby SPSS 19 and Sigma Plot 11 software.The result of this study showed there is a significant relationshipbetween vaccine degradation rate calculated with TCID50 and Real-Time PCR method (p<0.05). ThereforeReal-Time PCR is a good complement or appropriate replacement to traditional methods.Titration methods based on cell culture are gold tests for titration of viral vaccines and estimation of heat stability but Real-Time PCR technique can also be used for this goals. This method is faster, cheaper and easier than TCID50

Fault diagnosis for uncertain networked systems

Fault diagnosis has been at the forefront of technological developments for several decades. Recent advances in many engineering fields have led to the networked interconnection of various systems. The increased complexity of modern systems leads to a larger number of sources of uncertainty which must be taken into consideration and addressed properly in the design of monitoring and fault diagnosis architectures. This chapter reviews a model-based distributed fault diagnosis approach for uncertain nonlinear large-scale networked systems to specifically address: (a) the presence of measurement noise by devising a filtering scheme for dampening the effect of noise; (b) the modeling of uncertainty by developing an adaptive learning scheme; (c) the uncertainty issues emerging when considering networked systems such as the presence of delays and packet dropouts in the communication networks. The proposed architecture considers in an integrated way the various components of complex distributed systems such as the physical environment, the sensor level, the fault diagnosers, and the communication networks. Finally, some actions taken after the detection of a fault, such as the identification of the fault location and its magnitude or the learning of the fault function, are illustrated

Active fault detection in closed-loop dynamic systems

L'objectif de cette thèse est de développer une nouvelle méthodologie pour la détection active de défaillances, basée sur approche multimodèle et robuste des fautes. Ce travail prolonge des recherches effectuées dans le projet Metalau de l'Inria. L'apport essentiel de cette thèse est la prise en compte de modèles évoluant en boucle fermée. On utilise une approche multi-modèle pour modéliser le modèle en fonctionnement normal et le modèle défaillant. Les avantages potentiels de l'utilisation d'un feedback dynamique linéaire et ses propriétés de robustesse sont analysés dans la construction de signaux de détection auxiliaires. On compare les résultats obtenus avec ceux du cas boucle ouverte. La formulation du problème de détection active dans le cas d'un modèle en boucle fermée est nouvelle et repose sur la prise en considération de la norme du signal de détection auxiliaire comme critère d'optimisation. On considère aussi des fonctions coût plus générales, telles celles qui sont utilisées pour mesurer la performance de feedbacks dans des problèmes de la théorie de la commande linéaire robuste. La solution complète repose sur la résolution de plusieurs problèmes d'optimisation non standardsThe aim is to develop a novel theory of robust active failure detection based on multi-model formulation of faults. The original method was already proposed by the Metalau group of INRIA. We have continued to work on the extension of this approach to more general cases. The focus is on the effects of feedback on the previous approach. The multi-model approach is still used to model the normal and the failed systems; however the possible advantages of using linear dynamic feedback in the construction of the auxiliary signal for robust fault detection is considered and the results are compared to the previously developed open-loop setup. An original formulation of the active fault detection problem using feedback is developed. The norm of the auxiliary signal is considered as a possible cost criterion. Also, we have considered a more general cost function that has already been used for measuring the performance of feedback configurations in Linear Control Theory. We have given a complete solution to this problem. In order to find a complete solution, several mathematical problems are solve

Input-Output Subspace-Based Fault Detection

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Input Design for subspace based fault detection

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Auxiliary input design for stochastic subspace-based structural damage detection

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Distributed input and state estimation for linear discrete-time systems

International audienceThis paper provides a solution for distributed input and state estimation, simultaneously. A set of sensors with the capability of exchanging information is used to collect data from a discrete-time system. Various distributed implementations of Kalman filter have already been developed to track system states in such a setup when the system is subject to noise with known stochastic properties. However, practical systems might be subject to unknown input signals as well as stochastic noise, which leads to a biased state estimation. This study proposes new distributed filter that calculate state estimation in the presence of unknown inputs. In addition, the filter provides an estimation of the unknown inputs. A consensus-based distributed estimation algorithm is proposed in this paper. The algorithm gives an optimal unbiased minimum variance estimation if perfect consensus is achieved. Simulation results show the efficiency of the method

Joint Input and State Estimation for Linear Discrete-Time Networked Systems

International audienceThis paper proposes a distributed method for jointly estimating the input and the state of a given system observed through a sensor network. Traditionally, an unbiased state estimation can be obtained by using distributed Kalman filters if the system is subject to noise with known stochastic properties. Unfortunately, if the system is subject to a completely unknown input, representing faults, unknown disturbances or unmodeled dynamics, the estimated state is no longer unbiased. This study proposes new distributed filters that allow carrying out an unbiased state estimation in the presence of the unknown input. In addition, the proposed filters provide an estimation of the unknown inputs. Herein, it is assumed that the unknown input affects sensor measurements as well as system states. Two consensus-based distributed estimation algorithms are provided in this paper. The first algorithm gives an optimal minimum variance estimation if perfect consensus is achieved while the second algorithm provides a suboptimal solution with less computation effort