Ondrej Kreibich Wireless Diagnostic Methods in an Aerospace Application
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Czech Technical University in Prague Faculty of Electrical Engineering DOCTORAL THESIS OndˇrejKreibich Wireless diagnostic methods in an aerospace application Department of Measurement Supervisor of the doctoral thesis: doc. Ing. Radislav Sm´ıd,PhD.ˇ Study programme: Electrical Engineering and Information Technology Specialization: Air Traffic Control February 2014 Acknowledgement There are a number of people without whom this thesis might not have been written, and to whom I am greatly indebted. First and foremost, I have to thank my parents and my brother for their love and support throughout my life. A very special acknowledgement goes to my girlfriend Ester, who loved and supported me during the final, critical months of my dissertation. I would like to give my sincerely thanks to my supervisor, Associate Prof. Radislav Smid, PhD., for his guidance and support throughout my studies, and especially for his confidence in me. His comments and questions have helped me greatly in completing the manuscript. I am grateful to him for discussing and inter- preting some of the results presented in this thesis. Finally, my thanks to all my friends who have supported me or influenced me along the way. I declare that I carried out this doctoral thesis independently, and only with the cited sources, literature and other professional sources. In Prague date ............ signature of the author N´azevpr´ace:Metody bezdr´atov´ediagnostiky v leteck´ych aplikac´ıch Autor: Ing. OndˇrejKreibich Katedra: Katedra mˇeˇren´ı, CVUTˇ v Praze, FEL Vedouc´ıdisertaˇcn´ıpr´ace: doc. Ing. Radislav Sm´ıd,Ph.D.,ˇ Katedra mˇeˇren´ı Abstrakt: Strategie pl´anovan´e´udrˇzby je zaloˇzenana pr˚ubˇeˇzn´emsledov´an´ıstavu zaˇr´ızen´ıse syst´ememvˇcasn´ehovarov´an´ıpˇrednestandardn´ımistavy v chov´an´ısle- dovan´ehozaˇr´ızen´ı. Vyuˇzit´ıbezdr´atov´etechnologie WSN v t´etooblasti by pˇrineslo ˇraduv´yhod vych´azej´ıc´ıch z konstrukce senzorov´ehouzlu s´ıtˇe,ke kter´emu nevede ˇz´adn´ypˇr´ıvod. Takov´eˇreˇsen´ı usnadn´ı mont´aˇzsenzor˚una tˇeˇzko dostupn´am´ısta, ale z´aroveˇnotev´ır´amoˇznostipro zcela nov´eaplikace, napˇr´ıkladmˇeˇren´ına pohy- bliv´ych ˇc´astech zaˇr´ızen´ı.Aby takov´ysyst´emmohl b´ytnasazen do pr˚umyslov´epraxe, je potˇrebazaruˇcit spolehliv´ypˇrenosinformace mezi senzorov´ymiuzly a br´anou napojenou na nadˇrazen´ykontrol´er,ˇcipoˇc´ıtaˇc. Tato pr´acenavrhuje syst´empro sledov´an´ıstavu stroj˚uzaloˇzen´yna technologii WSN schopn´ypˇrekonat faleˇsn´ein- dikace zp˚usoben´edoˇcasnouztr´atoudat, ruˇsen´ımsign´alunebo pˇrenosemneplatn´ych dat a to za pouˇzit´ı multi { senzorov´edatov´ef´uzerozhoduj´ıc´ı se dle parametru kvality, zas´ılan´emsenzorov´ymuzlem spolu s daty. Tento ukazatel je zaloˇzenna stavu senzorov´ehouzlu (nap´ajen´ı,s´ılasign´alu)a porovn´an´ıaktu´aln´ıhodnoty s hod- notami v pˇredchoz´ıch datov´ych z´aznamech. Algoritmus datov´efuze rovnˇeˇzten- to indik´atorposkytuje. Tento nov´ypˇr´ıstupumoˇzˇnujeˇs´ıˇren´ıinformace o nejistotˇe mˇeˇren´ehodnoty ze zdrojov´ehouzlu aˇzk br´anˇea z´aroveˇnvyˇrazujeneplatn´adata na uzlech datov´efuze. T´ımmoˇznostdegradace pos´ılan´ediagnostick´einformace znaˇcnˇe kles´a.Pˇrenosrychl´ych sign´al˚uje zajiˇstˇenextrakc´ıa pˇrenosem pˇr´ıznak˚uze surov´ych dat, t´ımdoch´az´ık ´uspoˇrepˇrenosov´eˇs´ıˇrkyp´asmas´ıtˇe.Koncept byl experiment´alnˇe ovˇeˇrennejen matematickou simulac´ı, ale i na re´aln´emWSN hardware (Imote2). Pˇredpokl´adan´aefektivita syst´emu byla vyhodnocena pomoc´ıpomˇerusign´al/ ˇsum (SNR) a vlastn´ımdetektorem ˇcetnostiv´yskytuchyb (FAR). V´ysledky potvrzuj´ı,ˇze se navrhovan´ypˇr´ıstup vyrovn´adr´atov´emu propojen´ısenzoru s mˇeˇric´ı´ustˇrednou.A proto lze takov´ysyst´em aplikovat i na kritick´azaˇr´ızen´ı,jako jsou pohonn´ejednotky ultralehk´ych letadel, kde se syst´emvˇcasn´ekontroly z´avad doposud nevyuˇz´ıv´a. Kl´ıˇcov´aslova: Bezdr´atov´esenzorov´es´ıtˇe(WSN), Sledov´an´ıstavu stroj˚u(MCM), Technick´adiagnostika v letectv´ı,Datov´af˚uze I Title: Wireless diagnostic methods in an aerospace application Author: Ing. OndˇrejKreibich Department: Department of Measurement Supervisor: doc. Ing. Radislav Sm´ıd,Ph.D.,ˇ Department of Measurement Abstract: The early alert monitoring system for an effective scheduled maintenance strategy based on a wireless technology requires reliable transfer of diagnostic in- formation between the sensor and the gateway. This thesis presents a WSN-based machine condition monitoring (MCM) system capable of overcoming a false indica- tion caused by temporary loss of data, signal interference or invalid data. We use multi-sensor fusion driven by a quality parameter, produced by each sensor node according to the data history outliers and the actual state of the node. The fusion node also provides a quality evaluation on its output. This novel approach enables the propagation of information about the uncertainty of a measured value from the source node to the sink node. Thus potential degradation of acquired or transferred diagnostic information is minimized. Instead of raw data the signal features are transferred, so that bandwidth savings are improved considerably. The proposed concept was experimentally verified on real WSN hardware. The performance eval- uated using the Signal-to-Noise ratio and false alarm rate detection demonstrates the effectiveness of the proposed approach. The results confirm that the proposed system has similar reliability to a sensor connected by wire to a central unit. The machine condition monitoring system based on WSN with multi-sensor fusion is able to monitor a critical application, and even to monitor light aircraft powerplants. Keywords: Multisensor Information Fusion, Machine Condition Monitoring, Indus- trial Wireless Sensor Networks, Condition-based Monitoring in Aviation II List of Figures Figure 1.1: Cylinder head damage . .2 Figure 1.2: FFT indication examples of mechanical looseness . .5 Figure 2.1: OSA CBM Compliant Modular System . .9 Figure 2.2: Distributed data processing within CM standard architecture . 10 Figure 2.3: An example of the most widely-used centralized CM system . 14 Figure 2.4: An example of the SKF broadband centralized wireless CM system 15 Figure 2.5: An example of the TIMKEN low-energy consumption centralized wireless CM system . 15 Figure 2.6: Fundamental structure of a sensor node . 17 Figure 2.7: The Mica experimental platform by Crossbow . 18 Figure 2.8: EM35x WSN System-on-Chip by Silicon Labs . 19 Figure 2.9: IRTs connecting rod wireless measurement technology . 20 Figure 2.10: WSN network topologies . 24 Figure 2.11: Street lighting monitoring based on WSN . 24 Figure 4.1: WSN monitoring system based on information fusion . 34 Figure 4.2: Sensor node structure for MCM . 38 Figure 4.3: Compression of the amplitude spectrum . 38 Figure 4.4: Reliability graph of parallel connected components . 39 Figure 4.5: Feature fusion level arrangements . 40 Figure 4.6: A history buffer waveform construction at one feature sample position . 44 Figure 4.7: Formation of a quality indicator with respect to outliers . 45 Figure 5.1: Sensor node scheme . 50 Figure 5.2: Fusion node scheme . 53 Figure 5.3: The Dempster-Shafer-based data fusion process . 55 Figure 5.4: The fuzzy logic based data fusion process . 57 Figure 6.1: Fundamental WSN Matlab code flowchart . 61 Figure 6.2: Sensor node function flowchart in Matlab . 63 Figure 6.3: Imote2 stackable boards . 65 Figure 6.4: Verification of proposed methods . 66 Figure 6.5: Sensor node flowchart in TinyOS . 68 Figure 6.6: Fusion node flowchart in TinyOS . 69 Figure 7.1: Comparison of quality indicators . 75 Figure 7.2: Envelope thresholds for determining the false alarm rate (FAR) . 76 III Figure 7.3: Dependence of FAR on segment size . 77 Figure 7.4: Dependence of computing time consumption on size of segment . 78 Figure 7.5: Frequency spectrum comparison . 79 Figure 7.6: Data compression setting . 80 Figure 7.7: NI control panel . 80 Figure 7.8: Placement of the accelerometers in the vibrodiagnostics simulator 81 Figure 7.9: Fusion efficiency - real measurement . 83 Figure 7.10: Data fusion from three sensor nodes . 84 IV List of Tables Table 2.1: WSN standardized communication features . 28 Table 4.1: Classification of the type, source and sensing method of diag- nostic signals . 35 Table 4.2: Classification of a signal processing method on the basis of the fundamental WSN systems . 42 Table 5.1: Computation of trapezoidal function parameters . 52 Table 5.2: Generic truth table for 3 nodes . 56 Table 7.1: Pseudo-random signal sequence parameters . 76 Table 7.2: Optimal fuzzy-logic based fusion settings . 78 Table 7.3: Fusion node evaluation by artificial signals . 81 Table 7.4: Evaluation of the fusion node by real vibrodiagnostic signals . 82 Table 7.5: Summary of results . 85 V VI Acronyms AIDS Aircraft Integrated Data System BITE Built-in Test Equipment CAT Commercial Air Transport CBM Condition Based Maintenance CFDS Centralized Fault Display System CM Condition Monitoring CMS Central Maintenance System EASA European Aviation Safety Agency ECAM Electronic Centralised Aircraft Monitor EVMU Engine Vibration Monitoring Unit FADEC Full Authority Digital Engine Control GA General Aviation HP High Pressure ICAO International Civil Aviation Organization IWSN Industrial Wireless Sensor Network LP Low Pressure MCDU Multifunctional Control and Display Unit MCM Machine Condition Monitoring MSG-3 Maintenance Steering Group-3 MTOM Maximum Take Off Mass ODMs Oil Debris Monitoring system OSI Open Systems Interconnection SDAC Systems Data Acquisition Concentrator VII SGU Symbol Generator Unit VLA Very Light Aircraft VLJ Very Light \personal" Jets WSN Wireless Sensor Network ZDO ZigBee device object VIII Contents 1 Introduction 1 1.1 Aviation safety performance . .2 1.2 Maintenance scheme in current aviation . .3 1.3 Introduction of an early warning system for light aircraft . .4 1.3.1 Early warning MCM system based on a Wireless Sensor Network4 2 State of the art 7 2.1 General concept of condition monitoring . .7 2.1.1 CBM, Supervision and Fault Management .