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INTEGRATION OF HEALTH MONITORING SYSTEM FOR COMPOSITE ROTORS P. - PDF document

18 TH INTERNATIONAL CONFERENCE ON COMPOSITE MATERIALS INTEGRATION OF HEALTH MONITORING SYSTEM FOR COMPOSITE ROTORS P. Kostka 1 * , K. Holeczek 2 , A. Filippatos 2 , W. Hufenbach 1 1 Institute of Lightweight Engineering and Polymer Technology,


  1. 18 TH INTERNATIONAL CONFERENCE ON COMPOSITE MATERIALS INTEGRATION OF HEALTH MONITORING SYSTEM FOR COMPOSITE ROTORS P. Kostka 1 * , K. Holeczek 2 , A. Filippatos 2 , W. Hufenbach 1 1 Institute of Lightweight Engineering and Polymer Technology, Technische Universität Dresden, Dresden, Germany 2 European Centre for Emerging Materials and Processes Dresden, Technische Universität Dresden, Dresden, Germany * Corresponding author( pawel.kostka@ilk.mw.tu-dresden.de ) Keywords : lightweight structures, polymer technology, polymer-matrix composites, multi- functional composites, structural health monitoring, active vibration damping 1 Summary indicator of several different failure modes such as A concept of a combined material-integrated fibre failure or inter fibre failure. Cawley et al. [3] structural health monitoring and active vibration describes that not only the stiffness but also the damping system is proposed. Using a common set material damping is dependent on the state of of sensor and actuator components integrated in damage in the fibre reinforced composites. An a composite rotor, the system allows the control of important practical consequence is a correlation the structural dynamic behaviour under relevant between the changes in structural dynamical behaviour represented e.g. by modal properties and operating conditions as well as the detection of a progressing damage. A theoretical and experimental the damage state of the composite structure. An validation of this concept was conducted on the appropriate interpretation of such changes and their example of a complex shaped carbon fibre patterns could be used for an assessment of the reinforced composite structure. progressing damage in order to avoid critical operating conditions. The achievable assessment resolution is however limited through the frequency 2 Introduction bandwidth, the number of sensors and the resulting Fibre reinforced composites offer, in comparison to number of observable natural frequencies [3]. classical materials, excellent material properties e.g. According to Sohn [1] there are five levels of high specific strength and stiffness as well as damage identification: existence, location, type, adjustable damping properties. Thus, a growing extent and prognosis of remaining lifecycle. The interest in automotive, aerospace and other weight- here proposed diagnostic model obtained the second relevant applications dealing with dynamically level of damage identification based on the loaded structures is noticeable. vibration signals from additional material-integrated The performance of nearly all in-service composite functional elements. Such embedded sensors were structures is altered by the exposure to severe used for the monitoring of the structural dynamic environmental and operational conditions as well as behaviour direct on the structure. by damage caused by fatigue, impact, abrasion and The proposed structural health monitoring system is overload or operator abuse. The aforementioned structured as an inverse problem diagnostic model, influences can have serious consequences on the where the damage parameters, e.g. damage reliability, the maintenance costs and the presence or location, are calculated based on the operational capability of the structure. Therefore the modal properties. on-line monitoring of safety relevant structures concerning their material degradation and unexpected damage are of major concern in 3 Problem Definition composite applications [1]. In the former activities of the research group, Numerous researchers [1, 2] identify the complex shaped carbon fibre reinforced rotor degradation of the structural stiffness as a good structures with integrated active vibration damping

  2. (AVD) systems were developed [4, 5]. The 5 Experimental Procedure and Vibration Signal presented investigation focuses on an extension of Analysis this system mainly by the implementation of Due to the large number of possible combinations additional vibration-based structural health of failure modes, damage extents and their monitoring (SHM) functions. positions, the structural vibration behaviour was In order to allow an estimation of the structural changed during the experimental procedure in a integrity using the existing AVD hardware, the simplified way by attaching different small masses software of the existing controller block (Fig. 1) to the structure. Three different masses: 2 g, 5 g and was extended. On the one hand, it was necessary to 10 g were attached separately and independently in implement an instant calculation of modal 110 uniformly distributed surface points on the properties and on the other hand, appropriate investigated 400 g heavy blade (Fig. 2). For each diagnostic models, consisting of inference rule-sets, mass and its position, the structure was excited with had to be developed and implemented. the integrated actuator in a broad frequency band using white noise signal for a specific period of The goal of the proposed approach was to identify the discrete changes of vibration properties that time. could be correlated with the relevant changes of the The resulting vibration signals were measured with structural properties. Additionally, the achievable the integrated sensor, analogue filtered and cleansed resolution and the accuracy of the change in the signal conditioning unit by removing the identification were estimated. trends in order to reduce the systematic errors. The obtained signals were used for the parameter estimation of autoregressive linear prediction 4 Configuration of the Investigated Active models using the Burg method. These parametric Structure models of the measured signals were subsequently A carbon fibre reinforced scaled blade of an aero applied for the determination of power spectral engine fan (Fig. 2) with integrated AVD system densities, which were used as an input to a semi- was analyzed. The macro fibre composite patch and automatic algorithm of natural frequency detection. semiconductor strain gauge were used for the The obtained damage-sensitive features: natural deflection actuation and for the vibration frequencies and spectral densities were stored in measurement, respectively. one data set required for the machine learning The distribution of the integrated elements was procedure (Fig. 4) used during the identification of optimized for the maximal damping performance of diagnostic models. the first eigenmode, which was identified as the Two separate diagnostic models in form of rule- most critical during operation of the investigated based classifiers were developed and implemented. structure. The integrated vibration measurement The first classifier delivers the information about system was however sufficiently sensitive to record the existence of an additional mass and therefore the structural dynamical response up to the first could be associated with first level of damage three natural frequencies (Fig. 3), which were identification. The distinguishable data clusters subsequently used by the structural health were named: ‘Change’ and ‘No Change’ in order to monitoring function to assess the structural describe the presence or absence of the additional integrity. mass, respectively. The second classifier The strain gauge, the piezoelectric actuator, the distinguishes between the mass in blades tip conducting paths and the external connectors were (‘Upper Change’) and root region (‘Lower encapsulated between two thin polyimide films. Change’) and hence it is connected with the second The resulting self-contained active layer was stage of damage identification – the damage subsequently integrated into the structure during the location. resin transfer moulding consolidation procedure. The diagnostic models were inducted as deterministic decision trees (Fig. 5) using an inductive learning method similar to the C4.5 classification algorithm [6]. Respective learning

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