Top

Neural Computing and Applications

Published in:

01-01-2020 | Original Article

A fault mode identification methodology based on self-organizing map

Authors: Sébastien Schwartz, Juan José Montero Jimenez, Michel Salaün, Rob Vingerhoeds

Published in: Neural Computing and Applications | Issue 17/2020

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

One of the main goals of predictive maintenance is to be able to trigger the right maintenance actions at the right moment in time building upon the monitoring of the health status of the concerned systems and their components. As such, it allows identifying incipient faults and forecasting the moment of failure at the earliest stage. Many different data-driven methods are used in such approaches (Naderi and Khorasani in 2017 IEEE 30th Canadian conference on electrical and computer engineering (CCECE), Windsor, ON, IEEE, pp 1–6, 2017. https://doi.org/10.1109/ccece.2017.7946715; Sarkar et al. in J Eng Gas Turbines Power 1338(8):081602, 2011. https://doi.org/10.1115/1.4002877; Svärd et al. in Mech Syst Signal Process 45(1):170–192, 2014. https://doi.org/10.1016/j.ymssp.2013.11.002; Pourbabaee et al. Mech Syst Signal Process 76–77:136–156, 2016. https://doi.org/10.1016/j.ymssp.2016.02.023). This work uses the self-organizing maps (SOMs) or Kohonen map, thanks to its ability to emphasize underlying behavior such as fault modes. An automatic fault mode detection is presented based on a SOM network and the kernel density estimation with as less as possible prior knowledge. The different SOM development steps are presented and the suitable solutions proposed to structure the approach are accompanied by mathematical methods. The generated maps are then used with kernel density analysis to isolate fault modes on them. Finally, a methodology is presented to identify the different fault modes. The work is illustrated with an aircraft jet engines case study.

previous article Exploring speed–accuracy tradeoff in reaching movements: a neurocomputational model

next article Neural networks catching up with finite differences in solving partial differential equations in higher dimensions

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Simon DL, Rinehart AW (2014) A model-based anomaly detection approach for analyzing streaming aircraft engine measurement data. In: Volume 6: Ceramics; controls, diagnostics and instrumentation; education; manufacturing materials and metallurgy, Düsseldorf, Germany. ASME, p V006T06A032. https://doi.org/10.1115/gt2014-27172

Naderi E, Meskin N, Khorasani K (2012) Nonlinear fault diagnosis of jet engines by using a multiple model-based approach. J Eng Gas Turbines Power 134(1):011602. https://doi.org/10.1115/1.4004152CrossRef

Zeng D, Zhou D, Tan C, Jiang B (2018) Research on model-based fault diagnosis for a gas turbine based on transient performance. Appl Sci 8(1):148. https://doi.org/10.3390/app8010148CrossRef

Naderi E, Khorasani K (2017) Data-driven fault detection, isolation and estimation of aircraft gas turbine engine actuator and sensors. In: 2017 IEEE 30th Canadian conference on electrical and computer engineering (CCECE), Windsor, ON. IEEE, pp 1–6. https://doi.org/10.1109/ccece.2017.7946715

Sarkar S, Jin X, Ray A (2011) Data-driven fault detection in aircraft engines with noisy sensor measurements. J Eng Gas Turbines Power 133(8):081602. https://doi.org/10.1115/1.4002877CrossRef

Svärd C, Nyberg M, Frisk E, Krysander M (2014) Data-driven and adaptive statistical residual evaluation for fault detection with an automotive application. Mech Syst Signal Process 45(1):170–192. https://doi.org/10.1016/j.ymssp.2013.11.002CrossRef

Pourbabaee B, Meskin N, Khorasani K (2016) Robust sensor fault detection and isolation of gas turbine engines subjected to time-varying parameter uncertainties. Mech Syst Signal Process 76–77:136–156. https://doi.org/10.1016/j.ymssp.2016.02.023CrossRef

Venkatasubramanian V (2005) Prognostic and diagnostic monitoring of complex systems for product lifecycle management: challenges and opportunities. Comput Chem Eng 29(6):1253–1263. https://doi.org/10.1016/j.compchemeng.2005.02.026CrossRef

Vingerhoeds RA, Janssens P, Netten BD, Aznar Fernández-Montesinos M (1995) Enhancing off-line and on-line condition monitoring and fault diagnosis. Control Eng Pract 3(11):1515–1528. https://doi.org/10.1016/0967-0661(95)00162-NCrossRef

10.

Montero Jimenez JJ, Vingerhoeds R (2018) Enhancing operational fault diagnosis by assessing multiple operational modes. In: MOSIM’18—Conférence Internationale de Modélisation, Optimisation et Simulation, Toulouse

11.

Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69. https://doi.org/10.1007/BF00337288MathSciNetCrossRefMATH

12.

Germen E, Başaran M, Fidan M (2014) Sound based induction motor fault diagnosis using Kohonen self-organizing map. Mech Syst Signal Process 46(1):45–58. https://doi.org/10.1016/j.ymssp.2013.12.002CrossRef

13.

Côme E, Cottrell M, Verleysen M, Lacaille J (2010) Aircraft engine health monitoring using self-organizing maps. In: Perner P (ed) Advances in data mining. Springer, Berlin, pp 405–417. https://doi.org/10.1007/978-3-642-14400-4_31CrossRef

14.

Cottrell M, Gaubert P, Eloy C, François D, Hallaux G, Lacaille J, Verleysen M (2009) Fault prediction in aircraft engines using self-organizing maps. In: Príncipe JC, Miikkulainen R (eds) Advances in self-organizing maps, vol 5629. Springer, Berlin, pp 37–44. https://doi.org/10.1007/978-3-642-02397-2_5CrossRef

15.

Katunin A, Amarowicz M, Chrzanowski P (2015) Faults diagnosis using self-organizing maps: a case study on the DAMADICS benchmark problem, pp 1673–1681. https://doi.org/10.15439/2015f26

16.

Yu H, Khan F, Garaniya V (2015) Risk-based fault detection using self-organizing map. Reliab Eng Syst Saf 139:82–96. https://doi.org/10.1016/j.ress.2015.02.011CrossRef

17.

Chen X, Yan X (2012) Using improved self-organizing map for fault diagnosis in chemical industry process. Chem Eng Res Des 90(12):2262–2277. https://doi.org/10.1016/j.cherd.2012.06.004CrossRef

18.

Dharshini R, Hemanandhini S (2016) Brain tumor segmentation based on self organising map and discrete wavelet transform. In: 2016 international conference on computer communication and informatics (ICCCI), Coimbatore, India. IEEE, pp 1–9. https://doi.org/10.1109/iccci.2016.7479960

19.

Peel L (2008) Data driven prognostics using a Kalman filter ensemble of neural network models. In: 2008 international conference on prognostics and health management. https://doi.org/10.1109/phm.2008.4711423

20.

Jolliffe I (2011) Principal component analysis. Springer, Berlin. https://doi.org/10.1007/b98835CrossRefMATH

21.

Sammon JW (1969) A nonlinear mapping for data structure analysis. IEEE Trans Comput 100(5):401–409. https://doi.org/10.1109/t-c.1969.222678CrossRef

22.

Wang T, Jianbo Y, Siegel D, Lee JA (2008) Similarity-based prognostics approach for remaining useful life estimation of engineered systems. In: 2008 international conference on prognostics and health management. IEEE, pp 1–6. https://doi.org/10.1109/phm.2008.4711421

23.

Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3:1157–1182MATH

24.

Jovic A, Brkic K, Bogunovic N (2015) A review of feature selection methods with applications. In: 2015 38th international convention on information and communication technology, electronics and microelectronics (MIPRO), Opatija, Croatia. IEEE, pp 1200–1205. https://doi.org/10.1109/mipro.2015.7160458

25.

Saeys Y, Inza I, Larranaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23(19):2507–2517. https://doi.org/10.1093/bioinformatics/btm344CrossRef

26.

Visalakshi S, Radha V (2014) A literature review of feature selection techniques and applications: review of feature selection in data mining. In 2014 IEEE international conference on computational intelligence and computing research. IEEE, Coimbatore, India, pp 1–6. https://doi.org/10.1109/iccic.2014.7238499

27.

Kohonen T (1997) Springer series in information sciences, vol 30. Springer, Berlin. https://doi.org/10.1007/978-3-642-97966-8CrossRef

28.

Kohonen T (2014) Unigrafia, Helsinki, Finland

29.

Zin ZM (2014) Cluster and visualize data using 3D self-organizing maps. In: 2014 11th international conference on ubiquitous robots and ambient intelligence (URAI). IEEE, pp 163–168. https://doi.org/10.1109/urai.2014.7057523

30.

Azcarraga A, Manalili S (2011) Design of a structured 3D SOM as a music archive, pp 188–197. https://doi.org/10.1007/978-3-642-21566-7_19

31.

Gorricha J, Lobo V (2012) Improvements on the visualization of clusters in geo-referenced data using self-organizing maps. Comput Geosci 43:177–186. https://doi.org/10.1016/j.cageo.2011.10.008CrossRef

32.

El Tobely T, Salem A (2005) Position detection of unexploded ordnance from airborne magnetic anomaly data using 3-D self organized feature map. In: Proceedings of the fifth IEEE international symposium on signal processing and information technology, 2005. IEEE, pp 322–327. https://doi.org/10.1109/isspit.2005.1577117

33.

Fujimura K, Masuda K, Fukui Y (2006) A consideration on the multi-dimensional topology in self-organizing maps. In: 2006 international symposium on intelligent signal processing and communications, IEEE, pp 825–828. https://doi.org/10.1109/ispacs.2006.364772

34.

Côme E, Cottrell M, Verleysen M, Lacaille J (2010) Self organizing star (SOS) for health monitoring. In: European conference on artificial neural networks, pp 99–104

35.

Tian J, Azarian MH, Pecht M (2014) Anomaly detection using self-organizing maps-based k-nearest neighbor algorithm. In: European conference of the prognostics and health management society

36.

Engelbrecht AP (2007) Computational intelligence: an introduction, 2nd edn. Wiley, Hoboken. https://doi.org/10.1002/9780470512517CrossRef

37.

Su M-C, Liu T-K, Chang H-T (1999) An efficient initialization scheme for the self-organizing feature map algorithm. In: IJCNN’99. International joint conference on neural networks. Proceedings (Cat. No. 99CH36339), vol 3. IEEE, pp 1906–1910. https://doi.org/10.1109/ijcnn.1999.832672

38.

Kohonen T (2001) Springer series in information sciences, vol 30, 3rd edn. Springer, Berlin. https://doi.org/10.1007/978-3-642-56927-2CrossRef

39.

Kaski S, Venna J, Kohonen T (2000) Coloring that reveals cluster structures in multivariate data. Aust J Intell Inf Process Syst 6:82–88

40.

Alahakoon D, Halgamuge SK, Srinivasan B (2000) Dynamic self-organizing maps with controlled growth for knowledge discovery. IEEE Trans Neural Netw 11(3):601–614. https://doi.org/10.1109/72.846732CrossRef

41.

Natita W, Wiboonsak W, Dusadee S (2016) Appropriate learning rate and neighborhood function of self-organizing map (SOM) for specific humidity pattern classification over Southern Thailand. Int J Model Optim 6(1):61–65. https://doi.org/10.7763/IJMO.2016.V6.504CrossRef

42.

Zhang W, Wang J, Jin D, Oreopoulos L, Zhang Z (2018) A deterministic self-organizing map approach and its application on satellite data based cloud type classification. In: Conference IEEE Big Data

43.

Saxena A, Goebel K, Simon D, Eklund N (2008) Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 international conference on prognostics and health management. IEEE, pp 1–9. https://doi.org/10.1109/phm.2008.4711414

44.

Kafadar K, Bowman AW, Azzalini A (1999) Applied smoothing techniques for data analysis: the kernel approach with S-PLUS. J Am Stat Assoc 94(447):982. https://doi.org/10.2307/2670015CrossRef

45.

Bowman AW, Azzalini A (1997) Applied smoothing techniques for data analysis: the kernel approach with S-Plus illustrations, vol 18. Oxford University Press, OxfordMATH

46.

Frederick DK, DeCastro JA, Litt JS (2007) User’s guide for the commercial modular aero-propulsion system simulation (C-MAPSS)

47.

Hu C, Youn BD, Wang P, Taek Yoon J (2012) Ensemble of data-driven prongostic algorithms for robust prediction of remaining useful life. Reliab Eng Syst Saf 103:120–135. https://doi.org/10.1016/j.ress.2012.03.008CrossRef

48.

Fusco G, Perez J (2019) Bayesian network clustering and self-organizing maps under the test of Indian Districts. A comparison. Cybergeo. https://doi.org/10.4000/cybergeo.31909CrossRef

49.

Zhang X, Yao L, Wang X, Monaghan J, Mcalpine D, Zhang Y (2019) Cs Eess Q-Bio. arXiv:1905.04149

Title: A fault mode identification methodology based on self-organizing map
Authors: Sébastien Schwartz
Juan José Montero Jimenez
Michel Salaün
Rob Vingerhoeds
Publication date: 01-01-2020
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 17/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04692-x

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 17/2020

Purities prediction in a manufacturing froth flotation plant: the deep learning techniques

An enhanced sitting–sizing scheme for shunt capacitors in radial distribution systems using improved atom search optimization

Neural network approach for solving nonlocal boundary value problems

On self-organised aggregation dynamics in swarms of robots with informed robots

Mutual-manifold regularized robust fast latent LRR for subspace recovery and learning

Prediction of fluid pattern in a shear flow on intelligent neural nodes using ANFIS and LBM

Premium Partner