Damage Tolerant Active Contro l: Concept and State of the Art

Damage tolerant active control is a new research area relating to fault tolerant control design applied to mechanical structures. It encompasses several techniques already used to design controllers and to detect and to diagnose faults, as well to monitor structural integrity. Brief reviews of the common intersections of these areas are presented, with the purpose to clarify its relations and also to justify the new controller design paradigm. Some examples help to better understand the role of the new area

Principal component analysis and perturbation theory–based robust damage detection of multifunctional aircraft structure

A fundamental problem in structural damage detection is to define an efficient feature to calculate a damage index. Furthermore, due to perturbations from various sources, we also need to define a rigorous threshold whose overtaking indicates the presence of damages. In this article, we develop a robust damage detection methodology based on principal component analysis. We first present an original damage index based on projection of the separation matrix, and then, we drive a novel adaptive threshold that does not rely on statistical assumptions. This threshold is analytic, and it is based on matrix perturbation theory. The efficiency of the method is illustrated using simulations of a composite smart structure and experimental results performed on a conformal load-bearing antenna structure laboratory test

Perturbation Analysis for Robust Damage Detection with Application to Multifunctional Aircraft Structures

The most widely known form of multifunctional aircraft structure is smart structures for structural health monitoring (SHM). The aim is to provide automated systems whose purposes are to identify and to characterize possible damage within structures by using a network of actuators and sensors. Unfortunately, environmental and operational variability render many of the proposed damage detection methods difficult to successfully be applied. In this paper, an original robust damage detection approach using output-only vibration data is proposed. It is based on independent component analysis and matrix perturbation analysis, where an analytical threshold is proposed to get rid of statistical assumptions usually performed in damage detection approach. The effectiveness of the proposed SHM method is demonstrated numerically using finite element simulations and experimentally through a conformal load-bearing antenna structure and composite plates instrumented with piezoelectric ceramic materials.FUI MSIE (Pole Astech

Methodology for the Diagnosis of Hydromechanical Actuation Loops in Aircraft Engines

This document provides a method for on-board monitoring and on-ground diagnosis of a hydromechanical actuation loop such as those found in aircraft engines. First, a complete system analysis is performed to understand its behaviour and determine the main degradation modes. Then, system health indicators are defined and a method for their real time on-board extraction is addressed. Diagnosis is performed on-ground through classification of degradation signatures. To parameterize on-ground treatment, both a reference healthy state of indicators and degradations signatures are needed. The healthy distribution of indicators is obtained from data and a physics-based model is used to simulate degradations, quantify indicators sensibility and construct the signatures database. At last, algorithms are deployed and a statistical validation of the performances is conducted

Nonlinear structural damage detection based on cascade of Hammerstein models

Structural damages can result in nonlinear dynamical signatures that can significantly enhance their detection. An original nonlinear damage detection approach is proposed that is based on a cascade of Hammerstein models representation of the structure. This model is estimated by means of the Exponential Sine Sweep Method from only one measurement. On the basis of this estimated model, the linear and nonlinear parts of the output are estimated, and two damage indexes (DIs) are proposed. The first DI is built as the ratio of the energy contained in the nonlinear part of an output versus the energy contained in its linear part. The second DI is the angle between the subspaces obtained from the nonlinear parts of two set of outputs after a principal component analysis. The sensitivity of the proposed DIs to the presence of damages as well as their robustness to noise are assessed numerically on spring-mass-damper structures and experimentally on actual composite plates with surface-mounted PZT-elements. Results demonstrate the effectiveness of the proposed method to detect a damage in nonlinear structures and in the presence of noise

Selection and Validation of Health Indicators in Prognostics and Health Management System Design

Health Monitoring is the science of system health status evaluation. In the modern industrial world, it is getting more and more importance because it is a powerful tool to increase systems dependability. It is based on the observation of some variables extracted in operation reflecting the condition of a system. The quality of health monitoring strongly depends on the selection of these variables named health indicators. However, the issue in their selection is often underestimated and their validation is, of what is known, an untreated subject. In this paper, the authors introduce a complete methodology for the selection and validation of health indicators in health monitoring systems design. Although it can be applied either downstream on real measured data or upstream on simulated data, the true interest of the method is in the latter application. Indeed, a model-based validation can be integrated in the design phases of the system development process, thereby reducing potential controller retrofit costs and useless data storage. In order to simulate the distribution of health indicators, a well known surrogate model called Kriging is utilized. Eventually, the method is tested on a benchmark system: the high pressure pump of aircraft engines fuel systems. Thanks to the method, the set of health indicators was validated in system design phases and the monitoring is now ready to be implemented for in-service operation

Numerical Key Performance Indicators for the Validation of PHM Health Indicators with Application to a Hydraulic Actuation System

In order to perform Prognostic and Health Management (PHM) of a given system, it is necessary to define some relevant variables sensitive to the different degradation modes of the system. Those variables are named Health Indicators (HI) and they are the keystone of PHM. However, they are subject to a lot of uncertainties when computed in real time and the stochastic nature of PHM makes it hard to evaluate the efficiency of a HI set before the extraction algorithm is implemented. This document introduces Numerical Key Performance Indicators (NKPI) for the validation of HI computed only from data provided by numerical models in the upstream stages of a PHM system development process. In order to match as good as possible the reality, the multiple sources of uncertainties are quantified and propagated into the model. After having introduced the issue of uncertain systems modeling, the different NKPI are defined and eventually an application is performed on a hydraulic actuation system of an aircraft engine

A combined sensitivity analysis and kriging surrogate modeling for early validation of health indicators

To increase the dependability of complex systems, one solution is to assess their state of health continuously through the monitoring of variables sensitive to potential degradation modes. When computed in an operating environment, these variables, known as health indicators, are subject to many uncertainties. Hence, the stochastic nature of health assessment combined with the lack of data in design stages makes it difficult to evaluate the efficiency of a health indicator before the system enters into service. This paper introduces a method for early validation of health indicators during the design stages of a system development process. This method uses physics-based modeling and uncertainties propagation to create simulated stochastic data. However, because of the large number of parameters defining the model and its computation duration, the necessary runtime for uncertainties propagation is prohibitive. Thus, kriging is used to obtain low computation time estimations of the model outputs. Moreover, sensitivity analysis techniques are performed upstream to determine the hierarchization of the model parameters and to reduce the dimension of the input space. The validation is based on three types of numerical key performance indicators corresponding to the detection, identification and prognostic processes. After having introduced and formalized the framework of uncertain systems modeling and the different performance metrics, the issues of sensitivity analysis and surrogate modeling are addressed. The method is subsequently applied to the validation of a set of health indicators for the monitoring of an aircraft engine's pumping unit

Improving Aircraft Engines Prognostics and Health Management via Anticipated Model-Based Validation of Health Indicators

The aircraft engines manufacturing industry is subjected to many dependability constraints from certification authorities and economic background. In particular, the costs induced by unscheduled maintenance and delays and cancellations impose to ensure a minimum level of availability. For this purpose, Prognostics and Health Management (PHM) is used as a means to perform online periodic assessment of the engines’ health status. The whole PHM methodology is based on the processing of some variables reflecting the system’s health status named Health Indicators. The collecting of HI is an on-board embedded task which has to be specified before the entry into service for matters of retrofit costs. However, the current development methodology of PHM systems is considered as a marginal task in the industry and it is observed that most of the time, the set of HI is defined too late and only in a qualitative way. In this paper, the authors propose a novel development methodology for PHM systems centered on an anticipated model-based validation of HI. This validation is based on the use of uncertainties propagation to simulate the distributions of HI including the randomness of parameters. The paper defines also some performance metrics and criteria for the validation of the HI set. Eventually, the methodology is applied to the development of a PHM solution for an aircraft engine actuation loop. It reveals a lack of performance of the original set of HI and allows defining new ones in order to meet the specifications before the entry into service