Sensor network–based data-driven fault diagnosis in complex structures using limited prior knowledge is an interesting and hot topic in the literature. In this study, an integrated feature characterization and fuzzy decision method is developed based on a novel beam-like structure approach for fault diagnosis using available limited prior knowledge. Complex structures embedded with vibration sensors can be regarded as some virtual beam-like structures by considering the vibration transmission path from vibration sources to each sensor. The dynamic response of the structures in this vibration transmission path can demonstrate obvious fault features if there is a fault (typically, for example, cracks in connecting rods or around bolts, and bolt-loosening). These fault features can be effectively characterized and efficiently captured and utilized for fault diagnosis with a fuzzy decision method based on the virtual beam-like structure concept. This novel virtual beam-like structure approach needs only limited prior knowledge of the faults and does not require stationary response data. It is also not with respect to a specific structure design and is easy to implement within a sensor network. The effectiveness of the proposed method is well validated in the experiments for fault diagnosis including loosening bolts or cracks around bolts of complex structures such as bolted-base hanging structures in satellites.
LiSMDanMYangL. An airplane health management approach for civil aviation. In: Proceedings of the prognostics and system health management conference, Shenzhen, China, 24–25 May 2011, article no. 5939473. New York: IEEE.
2.
LiuJWangWMaFet al. A data-model-fusion prognostic framework for dynamic system state forecasting. Eng Appl Artif Intell2012; 25(4): 814–823.
3.
VemulapalliPKMongaVBrennanSN. Robust extrema features for time-series data analysis. IEEE T Pattern Anal2013; 35(6): 1464–1479.
4.
OlszewskiRT. Generalized feature extraction for structural pattern recognition in time series data. PhD Dissertation, Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, 2001.
5.
NixonMSLiuXUDirekogluCet al. On using physical analogies for feature and shape extraction in computer vision. Comput J2011; 54(1): 11–25.
6.
ScottGJKlaricMNDavisCHet al. Entropy-balanced bitmap tree for shape-based object retrieval from large-scale satellite imagery databases. IEEE T Geosci Remote2011; 49(5): 1603–1616.
7.
TanXYTriggsB. Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE T Image Process2010; 19: 1635–1650.
GuptaRDDashJKMukhopadhyayS. Rotation invariant textural feature extraction for image retrieval using eigen value analysis of intensity gradients and multi-resolution analysis. Pattern Recogn2013; 46(12): 3256–3267.
10.
LeeSYNguyenKDHuynhTCet al. Vibration-based damage monitoring of harbor caisson structure with damaged foundation-structure interface. Smart Struct Syst2012; 10(6): 517–546.
11.
HabtourEColeDPRiddickJCet al. Detection of fatigue damage precursor using a nonlinear vibration approach. Struct Control Health Monit. Epub ahead of print 16February2016. DOI: 10.1002/stc.1844.
12.
DibGUdpaL. Design and performance of optimal detectors for guided wave structural health monitoring. Struct Health Monit2016; 15(1): 21–37.
13.
LiuZLQuJZuoMJ. Fault level diagnosis for planetary gearboxes using hybrid kernel feature selection and kernel fisher discriminant analysis. Int J Adv Manuf Tech2013; 67(5–8): 1217–1230.
14.
HoseiniMLiuZLTaoTet al. Original feature used for condition monitoring of planetary gearboxes. Technical report, Department of Mechanical Engineering, University of Alberta, Edmonton, AB, Canada, 5May2011.
15.
KhazaeeMBanakarAGhobadianBet al. Fault detection of engine timing belt based on vibration signals using data-mining techniques and a novel data fusion procedure. Struct Health Monit2016; 15(5): 583–598.
16.
DargieWDenkoMK. Analysis of error-agnostic time- and frequency-domain features extracted from measurements of 3-D accelerometer sensors. IEEE Syst J2010; 4(1): 26–33.
17.
TkachDHuangHKuikenTA. Study of stability of time-domain features for electromyographic pattern recognition. J Neuroeng Rehabil2010; 7: 21.
18.
JiangLLYinHKLiXJet al. Fault diagnosis of rotating machinery based on multisensor information fusion using SVM and time-domain features. Shock Vib2014; 2014: 418178.
19.
WangCCKangYLiaoCC. Gear fault diagnosis in time domains via Bayesian networks. Trans Canadian Soc Mech Eng2013; 37(3): 665–672.
20.
SamantaBAl-BalushiKR. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech Syst Signal Pr2003; 17(2): 317–328.
21.
PrietoMDCirrincioneGEspinosaAGet al. Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE T Ind Electron2013; 60(8): 3398–3407.
22.
MoosavianAAhmadiHSakhaeiBet al. Support vector machine and k-nearest neighbour for unbalanced fault detection. J Qual Mainten Eng2014; 20(1): 65–75.
23.
WeiLKunZXiC. A new fault diagnosis method for cold test based on frequency analysis. Int J Model Ident Control2013; 18(2): 146–157.
24.
BouzidaATouhamiOIbtiouenRet al. Fault diagnosis in industrial induction machines through discrete wavelet transform. IEEE T Ind Electron2011; 58(9): 4385–4395.
25.
JavedKGouriveauRZerhouniNet al. Enabling health monitoring approach based on vibration data for accurate prognostics. IEEE T Ind Electron2015; 62(1): 647–656.
26.
LiaoRJWangKYangLJ. Binary particle swarm optimization selected time-frequency features for partial discharge signal classification. Int Rev Electr Eng2012; 7(5): 5905–5917.
27.
WongPKYangZVongCMet al. Real-time fault diagnosis for gas turbine generator systems using extreme learning machine. Neurocomputing2014; 128: 249–257.
28.
AmarMGondalIWilsonC. Vibration spectrum imaging: a novel bearing fault classification approach. IEEE T Ind Electron2015; 62(1): 494–502.
29.
RauberTWBoldtFDAVarejãoFM. Heterogeneous feature models and feature selection applied to bearing fault diagnosis. IEEE T Ind Electron2015; 62(1): 637–646.
30.
MingABZhangWQinZYet al. Envelope calculation of the multi-component signal and its application to the deterministic component cancellation in bearing fault diagnosis. Mech Syst Signal Pr2015; 50–51: 70–100.
31.
GhoshAMishraNSGhoshS. Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Inf Sci2011; 181 (4): 699–715.
32.
KennedyJEberhartR. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks (INNS), Piscataway, NJ, 27 November–1 December 1995, vol. 4, pp. 1942–1948. New York: IEEE.
33.
SAC Joint Venture. Recommended seismic evaluation and upgrade criteria for existing welded steel moment-frame buildings. FEMA-351, June2000. Federal Emergency Management Agency, http://www.nehrp.gov/pdf/fema351.pdf
34.
FanWQiaoPZ. Vibration-based damage identification methods: a review and comparative study. Struct Health Monit2011; 10(1): 83–111.
35.
DoraiswamiRChededL. Fault diagnosis of a sensor network: a distributed filtering approach. J Dyn Syst: T ASME2013; 135(5): 051002.
36.
KolluriSBhushanM. Audit of sensor networks for efficient fault diagnosis. J Process Control2013; 23(6): 881–893.
37.
MarkmillerJFCChangFK. Sensor network optimization for a passive sensing impact detection technique. Struct Health Monit2010; 9(1): 25–39.
38.
VanliOAZhangCNguyenAet al. A minimax sensor placement approach for damage detection in composite structures. J Intel Mat Syst Str2012; 23(8): 919–932.
39.
Yunusa-KaltungoASinhaJKNembhardAD. A novel fault diagnosis technique for enhancing maintenance and reliability of rotating machines. Struct Health Monit2015; 14(6): 604–621.
40.
Yunusa-KaltungoASinhaJK. Sensitivity analysis of higher order coherent spectra in machine faults diagnosis. Struct Health Monit2016; 15: 555–567.
41.
FlettJBoneGM. Fault detection and diagnosis of diesel engine valve trains. Mech Syst Signal Pr2016; 72–73: 316–327.
42.
JingXJLangZQ. Frequency domain analysis and design of nonlinear systems based on Volterra series expansion—a parametric characteristic approach. Cham: Springer International Publishing, 2015, pp. XV, 331.
43.
JingXJLiQK. A nonlinear decomposition and regulation method for nonlinearity characterization. Nonlin Dyn2016; 83: 1355–1377.
