Ondrej Kreibich Wireless diagnostic methods in an aerospace application

117 Pages·2014·8.51 MB·English

Czech Technical University in Prague
Faculty of Electrical Engineering
DOCTORAL THESIS
Ondˇrej Kreibich
Wireless diagnostic methods in an
aerospace application
Department of Measurement
Supervisor
ˇ
of the doctoral thesis: doc. Ing. Radislav Sm´ıd, PhD.
Study programme: Electrical Engineering and
Information Technology
Specialization: Air Traffic Control
February 2014

Acknowledgement
Thereareanumberofpeoplewithoutwhomthisthesismightnothavebeenwritten,
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

Na´zev pra´ce: Metody bezdra´tov´e diagnostiky v letecky´ch aplikac´ıch
Autor: Ing. Ondˇrej Kreibich
ˇ
Katedra: Katedra mˇeˇren´ı, CVUT v Praze, FEL
ˇ
Vedouc´ı disertaˇcn´ı pr´ace: doc. Ing. Radislav Sm´ıd, Ph.D., Katedra mˇeˇren´ı
Abstrakt: Strategie pla´novan´e u´drˇzby je zaloˇzena na pr˚ubˇeˇzn´em sledova´n´ı stavu
zaˇr´ızen´ı se syst´emem vˇcasn´eho varova´n´ı pˇred nestandardn´ımi stavy v chov´an´ı sle-
dovan´eho zaˇr´ızen´ı. Vyuˇzit´ı bezdra´tov´e technologie WSN v t´eto oblasti by pˇrineslo
ˇradu vy´hod vycha´zej´ıc´ıch z konstrukce senzorov´eho uzlu s´ıtˇe, ke kter´emu nevede
ˇza´dny´ pˇr´ıvod. Takov´e ˇreˇsen´ı usnadn´ı monta´ˇz senzor˚u na tˇeˇzko dostupna´ m´ısta,
ale za´rovenˇ otev´ıra´ moˇznosti pro zcela nov´e aplikace, napˇr´ıklad mˇeˇren´ı na pohy-
blivy´chˇc´astech zaˇr´ızen´ı. Aby takovy´ syst´em mohl by´t nasazen do pr˚umyslov´e praxe,
je potˇreba zaruˇcit spolehlivy´ pˇrenos informace mezi senzorovy´mi uzly a bra´nou
napojenou na nadˇrazeny´ kontrol´er, ˇci poˇc´ıtaˇc. Tato pr´ace navrhuje syst´em pro
sledova´n´ı stavu stroj˚u zaloˇzeny´ na technologii WSN schopny´ pˇrekonat faleˇsn´e in-
dikace zp˚usoben´e doˇcasnou ztra´tou dat, ruˇsen´ım sign´alu nebo pˇrenosem neplatny´ch
dat a to za pouˇzit´ı multi – senzorov´e datov´e fu´ze rozhoduj´ıc´ı se dle parametru
kvality, zas´ılan´em senzorovy´m uzlem spolu s daty. Tento ukazatel je zaloˇzen na
stavu senzorov´eho uzlu (nap´ajen´ı, s´ıla signa´lu) a porovn´an´ı aktu´aln´ı hodnoty s hod-
notami v pˇredchoz´ıch datovy´ch za´znamech. Algoritmus datov´e fuze rovnˇeˇz ten-
to indika´tor poskytuje. Tento novy´ pˇr´ıstup umoˇznˇuje ˇs´ıˇren´ı informace o nejistotˇe
mˇeˇren´e hodnoty ze zdrojov´eho uzlu aˇz k br´anˇe a za´rovenˇ vyˇrazuje neplatna´ data na
uzlech datov´e fuze. T´ım moˇznost degradace pos´ılan´e diagnostick´e informace znaˇcnˇe
klesa´. Pˇrenos rychly´ch sign´al˚u je zajiˇstˇen extrakc´ı a pˇrenosem pˇr´ıznak˚u ze surovy´ch
dat, t´ım docha´z´ı k u´spoˇre pˇrenosov´e ˇs´ıˇrky pa´sma s´ıtˇe. Koncept byl experimenta´lnˇe
ovˇeˇren nejen matematickou simulac´ı, ale i na rea´ln´em WSN hardware (Imote2).
Pˇredpokl´adana´ efektivita syst´emu byla vyhodnocena pomoc´ı pomˇeru signa´l / ˇsum
(SNR) a vlastn´ım detektorem ˇcetnosti vy´skytu chyb (FAR). Vy´sledky potvrzuj´ı, ˇze
se navrhovany´ pˇr´ıstup vyrovna´ dra´tov´emu propojen´ı senzoru s mˇeˇric´ı u´stˇrednou. A
proto lze takovy´ syst´em aplikovat i na kritick´a zaˇr´ızen´ı, jako jsou pohonn´e jednotky
ultralehky´ch letadel, kde se syst´em vˇcasn´e kontroly za´vad doposud nevyuˇz´ıv´a.
Kl´ıˇcova´ slova: Bezdr´atov´e senzorov´e s´ıtˇe (WSN), Sledov´an´ı stavu stroj˚u (MCM),
Technicka´ diagnostika v letectv´ı, Datov´a f˚uze
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Title: Wireless diagnostic methods in an aerospace application
Author: Ing. Ondˇrej Kreibich
Department: Department of Measurement
ˇ
Supervisor: doc. Ing. Radislav Sm´ıd, Ph.D., 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
machineconditionmonitoringsystembasedonWSNwithmulti-sensorfusionisable
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
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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: AnexampleoftheTIMKENlow-energyconsumptioncentralized
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
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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
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