Community Research and Development Information Service - CORDIS

FP7

SENCES Report Summary

Project ID: 255837
Funded under: FP7-JTI
Country: Greece

Periodic Report Summary 2 - SENCES (An open simulation environment for self-diagnosis and prognosis in composite aero-structures)


Project Context and Objectives:

The design of self – diagnostic structures usually requires several physical tests in order to evaluate the performance of an SHM system and the structure itself. SENCES project focuses on the development of a software tool called OSAS platform that will use validated analysis tools instead of expensive physical tests. With this platform, the designer will be able to model a self – diagnostic structure and perform diagnostics and prognostics using simulated data coming from Multilevel Structural Analysis. Moreover, the platform will have the ability to perform design optimisation and to select the best SHM system properties.

In the course of realising the OASAS platform the following activities have been carried out:

1. Approximation techniques have been developed to build computationally inexpensive surrogate models (or meta-models) to replace expensive-to-run multilevel analysis code. The developed meta-models will be used to produce valuable information to perform Diagnostics and Prognostics. Two meta-models are going to be generated for the multilevel analysis model. A global and a local meta-model. These surrogate models use sampled information from high–fidelity analysis codes in order to generate faster and lower–fidelity approximations. Sampled information is determined by the use of Design of Experiments (DoE), which create a set of points that the analysis response will be computed. A Permutation Genetic Algorithm (PermGA) method has been developed in order to generate a high – quality DoE. In order to build reliable surrogates, a Kriging predictor has been combined with a typical Response Surface Method (RSM). The developed Matlab code is developed in a Graphical User Interface environment and has three main procedures: Design of Experiments, Kriging Approximation and Adaptive LHD (for inherited DoE).

2. A Diagnostic theory and tool has been developed to detect and identify damage using simulated signal information and could be used to assess and optimise the reliability of SHM system. The model takes into account the signal characteristics and uncertainties (e.g. signal noise, signal threshold, etc.) and determines the detected damage parameters (type, size and location). It also takes into account the quantification of the uncertainty coming from the signal. Further to the original expectations, the theory has been complemented with the capability of determining the actual damage size characteristics using the results of the detected state as well as with updating schemes of the damage state when additional monitoring data becomes available, based on Bayesian statistics. Thus, it is able to represent the “true” damage state and its characteristics as a function of time/cycles.

3. A comprehensive and detailed Prognostic theoretical tool has been developed to compute the structural behaviour over time. Initially, the Probability of detecting a life – limiting damage is computed using updated information (multiple monitoring events) coming from different SHM systems with different attributes (mixed mode procedures). The next step is to compute the Probability of structural failure due to a non – detected damage for the above cases (multiple – mixed mode). The third step is to calculate the Probability of not detecting a life-limiting damage with a specified rate of occurrence. In the end the synthesis of the above is generalized in order to compute the Probability of Failure for various damage types in different subcomponents (or zones) of the structure. This quantity is very critical in the determination of the maintenance schedule.

Project Results:

During the 2nd year the work had to be concentrated on the completion of Methods and Tools WP leading to the completion of the OSAS platform components. The work was broken into various tasks reflecting the technological developments needed. Specifically, the work and related objectives briefly were:

WP1.1 Multi-level Analysis

Objective: The purpose of this task is to develop and use a Multilevel / Multi-scale Analysis model of a sensorised composite panel to compute i) structural response, ii) sensors integrity and iii) simulated signal coming from virtual sensors placed on the model of the structure, using a High Performance Computing (HPC) system. The model will be used to produce data in order to build accurate Meta-models in WP1.2.

Outcome: D1.1 – Report and model for Multilevel/Multi-scale Analysis

WP1.2 Meta-modeling

Objective: Approximation techniques will be applied to build computationally inexpensive surrogate models (or meta-models) to replace expensive-to-run multilevel analysis code(s) (WP1.1). The developed meta-models will be used in WP1.3 and WP1.4 to produce valuable information to perform Diagnostics and Prognostics.

Outcome: D1.2 – Report and S/W module to construct meta-models (surrogate models)

WP1.3 Diagnostics

Objective: A Diagnostic tool will be developed to detect and identify damage using simulated signal information from WP1.2 and ii) assess the reliability of SHM system. The developed tool will be used in WP1.4 to perform Prognostics.

Outcome: D1.3 – Report and S/W Diagnostics module

WP1.4 Prognostics

Objective: A Prognostic tool will be developed to compute the structural behaviour over time. The results will be used to follow a maintenance scheduling in WP1.5.

Outcome: D1.4 – Report and S/W Prognostics module

WP1.5 Maintenance

Objective: A probabilistic maintenance strategy based on the sensor readings will be followed. The reduction of the operational costs will have a major impact on the computation of life – cycle costs in WP2.2.

Outcome: D1.5 – Report and S/W Maintenance module

WP2.1 Platform specifications

Objective: The purpose of this work package is to i) identify the nature of the elements that will be embedded inside the OSAS platform and ii) to plan their appropriate integration. The outcome of this task will be used in integrating OSAS platform in WP2.3.

Outcome: D2.1 – Report on OSAS platform’s components /platform specifications

WP2.2 Platform specifications

Objective: The purpose of this work package is to provide simple Weight and Cost models to be integrated with Structural Reliability in the Optimisation framework of WP2.3.

Outcome: D2.1 – Report on OSAS platform’s components /platform specifications

WP2.3 Platform specifications

Objective: The purpose of this work package is the development of optimisation methods. Optimisation process will have two main objectives; to optimise the SHM system properties (sensors topology, threshold values) and the structure itself (geometry, materials). For this purpose, advanced multi-objective optimisation methods will be used.

Outcome: D2.1 – Report on OSAS platform’s components /platform specifications

Potential Impact:

The scope of this work is to develop an integrated environment called OSAS platform. This platform will be able to perform diagnostics and prognostics using exclusively simulated instead of real data. Moreover it will be able to optimise the design of composite aero – structures and choose the best SHM system properties. In Figure 1, a general flowchart of the platform function is shown. Impact simulation is performed using an appropriate structural model, which has geometry and material as input variables. The response will initially be used to reproduce realistic sensors signal using a sensors model that uses sensors topology and threshold values as inputs. The resulted signal will be correlated with the response in order to build a diagnostic model that will be able to provide information on possible damages, Probability of Detection (PoD) and Probability of False Alarm (PoFA). Damage information will be used to evaluate the future behaviour of the structure and to follow a sensor – advised maintenance strategy. On the first hand, PoD and PoFA will be used as objectives in order to optimise the SHM system properties, and on the other hand, cost and weight objectives will form a multi - objective structural optimisation.


Contact

DIMITRI BOFILIOS, (MANAGING DIRECTOR)
Tel.: +302109961860
Fax: +302109961019
Follow us on: RSS Facebook Twitter YouTube Managed by the EU Publications Office Top