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http://hdl.handle.net/10985/11775
Peaks Over Threshold–based detector design for structural health monitoring: Application to aerospace structures
REBILLAT, Marc; HMAD, Ouadie; KADRI, Farid; MECHBAL, Nazih
Structural health monitoring offers new approaches to interrogate the integrity of complex structures. The structural health monitoring process classically relies on four sequential steps: damage detection, localization, classification, and quantification. The most critical step of such process is the damage detection step since it is the first one and because performances of the following steps depend on it. A common method to design such a detector consists of relying on a statistical characterization of the damage indexes available in the healthy behavior of the structure. On the basis of this information, a decision threshold can then be computed in order to achieve a desired probability of false alarm. To determine the decision threshold corresponding to such desired probability of false alarm, the approach considered here is based on a model of the tail of the damage indexes distribution built using the Peaks Over Threshold method extracted from the extreme value theory. This approach of tail distribution estimation is interesting since it is not necessary to know the whole distribution of the damage indexes to develop a detector, but only its tail. This methodology is applied here in the context of a composite aircraft nacelle (where desired probability of false alarm is typically between 1024 and 1029) for different configurations of learning sample size and probability of false alarm and is compared to a more classical one which consists of modeling the entire damage indexes distribution by means of Parzen windows. Results show that given a set of data in the healthy state, the effective probability of false alarm obtained using the Peaks Over Threshold method is closer to the desired probability of false alarm than the one obtained using the Parzen-window method, which appears to be more conservative.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10985/117752018-01-01T00:00:00ZREBILLAT, MarcHMAD, OuadieKADRI, FaridMECHBAL, NazihStructural health monitoring offers new approaches to interrogate the integrity of complex structures. The structural health monitoring process classically relies on four sequential steps: damage detection, localization, classification, and quantification. The most critical step of such process is the damage detection step since it is the first one and because performances of the following steps depend on it. A common method to design such a detector consists of relying on a statistical characterization of the damage indexes available in the healthy behavior of the structure. On the basis of this information, a decision threshold can then be computed in order to achieve a desired probability of false alarm. To determine the decision threshold corresponding to such desired probability of false alarm, the approach considered here is based on a model of the tail of the damage indexes distribution built using the Peaks Over Threshold method extracted from the extreme value theory. This approach of tail distribution estimation is interesting since it is not necessary to know the whole distribution of the damage indexes to develop a detector, but only its tail. This methodology is applied here in the context of a composite aircraft nacelle (where desired probability of false alarm is typically between 1024 and 1029) for different configurations of learning sample size and probability of false alarm and is compared to a more classical one which consists of modeling the entire damage indexes distribution by means of Parzen windows. Results show that given a set of data in the healthy state, the effective probability of false alarm obtained using the Peaks Over Threshold method is closer to the desired probability of false alarm than the one obtained using the Parzen-window method, which appears to be more conservative.Peaks Over Threshold Method for Structural Health Monitoring Detector Design
http://hdl.handle.net/10985/10377
Peaks Over Threshold Method for Structural Health Monitoring Detector Design
HMAD, Ouadie; MECHBAL, Nazih; REBILLAT, Marc
Structural Health Monitoring (SHM) system offers new approaches to interrogate the integrity of structures. The most critical step of such systems is the damage detection step since it is the first and because performances of the following steps (damage localization, severity estimation…) depend on it. Care has thus to be taken when designing the detector. The objective of this communication is to discuss issues related to the design of a detector for the structural health monitoring of composite structures. The structure under monitoring is a substructure of an aircraft nacelle. In the absence of damage, the detector principle is to statistically characterize the healthy behavior of the structure. This characterization is based on the availability of a decision statistics synthesized from a damage index. Airline business models rely on Probability of False Alarms (Pfa) as main performance criterion. In general, the requirement on Pfa is 10E-9 which is very small. To determine the decision threshold, the approach we consider, consists to model the tail of the decision statistics using the Peaks Over Threshold method extracted from the extreme value theory (EVT). This method has been applied for different configuration of learning sample and probability of false alarm. This approach of tail distribution estimation is interesting since it is not necessary to know the distribution of the decision statistic to develop a detector. However, its main drawback is that it is necessary to have very large databases to accurately estimate decision thresholds to then decide the presence or absence of damage.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/103772015-01-01T00:00:00ZHMAD, OuadieMECHBAL, NazihREBILLAT, MarcStructural Health Monitoring (SHM) system offers new approaches to interrogate the integrity of structures. The most critical step of such systems is the damage detection step since it is the first and because performances of the following steps (damage localization, severity estimation…) depend on it. Care has thus to be taken when designing the detector. The objective of this communication is to discuss issues related to the design of a detector for the structural health monitoring of composite structures. The structure under monitoring is a substructure of an aircraft nacelle. In the absence of damage, the detector principle is to statistically characterize the healthy behavior of the structure. This characterization is based on the availability of a decision statistics synthesized from a damage index. Airline business models rely on Probability of False Alarms (Pfa) as main performance criterion. In general, the requirement on Pfa is 10E-9 which is very small. To determine the decision threshold, the approach we consider, consists to model the tail of the decision statistics using the Peaks Over Threshold method extracted from the extreme value theory (EVT). This method has been applied for different configuration of learning sample and probability of false alarm. This approach of tail distribution estimation is interesting since it is not necessary to know the distribution of the decision statistic to develop a detector. However, its main drawback is that it is necessary to have very large databases to accurately estimate decision thresholds to then decide the presence or absence of damage.Verification and Validation of Structural Health Monitoring Algorithms: A Maturation Procedure
http://hdl.handle.net/10985/10040
Verification and Validation of Structural Health Monitoring Algorithms: A Maturation Procedure
HMAD, Ouadie; FENDZI, Claude; MECHBAL, Nazih; REBILLAT, Marc
Structural Health Monitoring (SHM) system offers new approaches to interrogate the integrity of structures. However, their reliability has still to be demonstrated an quantified to enable confidence transition from R&D to field implementation. In general, SHM algorithms performances are illustrated by topography study but it is not sufficient in a reliability assessment context. In the sense, that there is no quantification of the performance. To address this key issue, a dedicated maturation procedure is proposed in this paper. It is strongly inspired from the six sigma procedure for processes improvement to gradually improve SHM algorithms in order to reach the required maturity level. This paper presents the application of this procedure to a damage SHM localization algorithm as case study. To address this issue, finite element models and experimentation on the monitored structure have been used. It is concluded with a need of a new specific SHM algorithm intrinsic maturity scale. These maturity scales can be defined with respect to the functions of the considered SHM algorithm and the type of the used data.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10985/100402015-01-01T00:00:00ZHMAD, OuadieFENDZI, ClaudeMECHBAL, NazihREBILLAT, MarcStructural Health Monitoring (SHM) system offers new approaches to interrogate the integrity of structures. However, their reliability has still to be demonstrated an quantified to enable confidence transition from R&D to field implementation. In general, SHM algorithms performances are illustrated by topography study but it is not sufficient in a reliability assessment context. In the sense, that there is no quantification of the performance. To address this key issue, a dedicated maturation procedure is proposed in this paper. It is strongly inspired from the six sigma procedure for processes improvement to gradually improve SHM algorithms in order to reach the required maturity level. This paper presents the application of this procedure to a damage SHM localization algorithm as case study. To address this issue, finite element models and experimentation on the monitored structure have been used. It is concluded with a need of a new specific SHM algorithm intrinsic maturity scale. These maturity scales can be defined with respect to the functions of the considered SHM algorithm and the type of the used data.