Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Feature Engineering and Health Indicator Construction for Fault Detection and Diagnostic

  • Chapter
  • First Online:
Control Charts and Machine Learning for Anomaly Detection in Manufacturing

Part of the book series: Springer Series in Reliability Engineering ((RELIABILITY))

  • 1404 Accesses

Abstract

Nowadays, the rapid growth of modern technologies in Internet of Things (IoT) and sensing platforms is enabling the development of autonomous health management systems. This can be done, in the first step, by using intelligent sensors, which provide reliable solutions for systems monitoring in real-time. Then, the monitoring data will be treated and analyzed in the second step to extract health indicators (HIs) for maintenance and operation decisions. This procedure called feature engineering (FE) and HI construction is the key step that decides the performance of condition monitoring systems. Hence, in this chapter we present a comprehensive review and new advances of FE techniques and HI construction methods for fault detection and diagnostic (FDD) of engineering systems. This chapter would also serve as an instructive guideline for industrial practitioners and researchers with different levels of experience to broaden their skills about system condition monitoring procedure.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Soualhi M, Nguyen KTP, Soualhi A, Medjaher K, Hemsas KE (2019) Health monitoring of bearing and gear faults by using a new health indicator extracted from current signals. Measurement 141:37–51

    Article  Google Scholar 

  2. Cheng S, Azarian MH, Pecht MG (2010) Sensor systems for prognostics and health management. Sens. (Basel, Switz.) 10:5774–5797

    Article  Google Scholar 

  3. Soualhi M, Nguyen KT, Medjaher K (2020) Pattern recognition method of fault diagnostics based on a new health indicator for smart manufacturing. Mech Syst Sig Process 142:106680

    Google Scholar 

  4. Nisbet R, Elder J, Miner G (2009) Data understanding and preparation. In: Nisbet R, Elder J, Miner G (eds) Handbook of statistical analysis and data mining applications. Academic Press, Boston, pp 49–75

    Chapter  Google Scholar 

  5. Yakout M, Berti-Équille L, Elmagarmid AK (2013) Don’t be SCAREd: use SCalable automatic REpairing with maximal likelihood and bounded changes. In: Proceedings of the 2013 international conference on management of data - SIGMOD 2013. ACM Press, New York, p 553

    Google Scholar 

  6. Haas D, Krishnan S, Wang J, Franklin MJ, Wu E (2015) Wisteria: nurturing scalable data cleaning infrastructure. Proc VLDB Endowment 8:2004–2007

    Article  Google Scholar 

  7. Subramaniam S, Palpanas T, Papadopoulos D, Kalogeraki V, Gunopulos D (2006) Online outlier detection in sensor data using non-parametric models. In: Proceedings of the 32nd international conference on very large data bases, VLDB 2006, VLDB Endowment, Seoul, Korea, pp 187–198

    Google Scholar 

  8. Berti-Équille L, Dasu T, Srivastava D (2011) Discovery of complex glitch patterns: a novel approach to quantitative data cleaning. In: 2011 IEEE 27th international conference on data engineering, Hannover, Germany, pp 733–744

    Google Scholar 

  9. Ratolojanahary R, Houé Ngouna R, Medjaher K, Junca-Bourié J, Dauriac F, Sebilo M (2019) Model selection to improve multiple imputation for handling high rate missingness in a water quality dataset. Expert Syst Appl 131:299–307

    Google Scholar 

  10. Sorzano COS, Vargas J, Montano AP (2014) A survey of dimensionality reduction techniques. arXiv:1403.2877 [cs, q-bio, stat]

  11. Nguyen KTP, et al (2018) Analysis and comparison of multiple features for fault detection and prognostic in ball bearings. In: PHM society European conference, Utrecht, The Netherlands, vol 4, pp 1–6

    Google Scholar 

  12. Shukla S, Yadav RN, Sharma J, Khare S (2015) Analysis of statistical features for fault detection in ball bearing. In: 2015 IEEE international conference on computational intelligence and computing research (ICCIC), Madurai, India, pp 1–7

    Google Scholar 

  13. Mahamad AK, Hiyama T (2008) Development of artificial neural network based fault diagnosis of induction motor bearing. In: 2008 IEEE 2nd international power and energy conference, Johor Bahru, Malaysia, pp 1387–1392

    Google Scholar 

  14. Qian Y, Yan R, Hu S (2014) Bearing degradation evaluation using recurrence quantification analysis and Kalman Filter. IEEE Trans Instrum Meas 63:2599–2610 (2014)

    Google Scholar 

  15. Li R, Sopon P, He D (2012) Fault features extraction for bearing prognostics. J Intell Manuf 23:313–321

    Article  Google Scholar 

  16. Silva JLH, Cardoso AJM (2005) Bearing failures diagnosis in three-phase induction motors by extended Park’s vector approach. In: 31st annual conference of IEEE industrial electronics society. IECON 2005, p 6

    Google Scholar 

  17. Zhou W, Habetler TG, Harley RG (2007) Bearing condition monitoring methods for electric machines: a general review, pp 3–6

    Google Scholar 

  18. Gebraeel N, Lawley M, Liu R, Parmeshwaran V (2004) Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Trans Ind Electron 51:694–700

    Article  Google Scholar 

  19. Kappaganthu K, Nataraj C (2011) Feature selection for fault detection in rolling element bearings using mutual information. J Vibr Acoust 133:061001 (2011)

    Google Scholar 

  20. Yu J (2012) Local and nonlocal preserving projection for bearing defect classification and performance assessment. IEEE Trans Ind Electron 59:2363–2376

    Article  Google Scholar 

  21. Fournier E, Picot A, Régnier J, Yamdeu MT, Andréjak JM, Maussion P (2015) Current-based detection of mechanical unbalance in an induction machine using spectral kurtosis with reference. IEEE Trans Ind Electron 62:1879–1887

    Article  Google Scholar 

  22. Gong X, Qiao W (2013) Bearing fault diagnosis for direct-drive wind turbines via current-demodulated signals. IEEE Trans Ind Electron 60:3419–3428

    Article  Google Scholar 

  23. Picot A, Obeid Z, Régnier J, Poignant S, Darnis O, Maussion P (2014) Statistic-based spectral indicator for bearing fault detection in permanent-magnet synchronous machines using the stator current. Mech Syst Signal Process 46:424–441

    Article  Google Scholar 

  24. Cong F, Chen J, Dong G (2012) Spectral kurtosis based on AR model for fault diagnosis and condition monitoring of rolling bearing. J Mech Sci Technol 26:301–306

    Article  Google Scholar 

  25. Leite VCMN, et al (2015) Detection of localized bearing faults in induction machines by spectral kurtosis and envelope analysis of stator current. IEEE Trans Ind Electron 62:1855–1865

    Google Scholar 

  26. Yazici B, Kliman G (1999) An adaptive statistical time-frequency method for detection of broken bars and bearing faults in motors using stator current. IEEE Trans Ind Appl 35:442–452

    Article  Google Scholar 

  27. Deekshit Kompella KC, Venu Gopala Rao M, Srinivasa Rao R (2018) Bearing fault detection in a 3 phase induction motor using stator current frequency spectral subtraction with various wavelet decomposition techniques. Ain Shams Eng J 9:2427–2439

    Google Scholar 

  28. Refaat SS, Abu-Rub H, Saad MS, Aboul-Zahab EM, Iqbal A (2013) ANN-based for detection, diagnosis the bearing fault for three phase induction motors using current signal. In: 2013 IEEE international conference on industrial technology (ICIT), Cape Town, South Africa, pp 253–258

    Google Scholar 

  29. Elbouchikhi E, Choqueuse V, Amirat Y, Benbouzid MEH, Turri S (2017) An efficient Hilbert-Huang transform-based bearing faults detection in induction machines. IEEE Trans Energy Convers 32:401–413

    Article  Google Scholar 

  30. Chandrashekar G, Sahin F (2014) A survey on feature selection methods. Comput Electr Eng 40:16–28

    Article  Google Scholar 

  31. Venkatesh B, Anuradha J (2019) A review of feature selection and its methods. Cybern Inform Technol 19:3–26

    MathSciNet  Google Scholar 

  32. Khaire UM, Dhanalakshmi R (2019) Stability of feature selection algorithm: a review. J King Saud Univer Comput Inform Sci 1–14

    Google Scholar 

  33. Nguyen KTP, Medjaher K (2020) An automated health indicator construction methodology for prognostics based on multi-criteria optimization. ISA Trans 1–16

    Google Scholar 

  34. Tang J, Alelyani S, Liu H (2014) Feature selection for classification: a review, data classification: algorithms and applications 37–64

    Google Scholar 

  35. Knöbel C, Marsil Z, Rekla M, Reuter J, Gühmann C (2015) Fault detection in linear electromagnetic actuators using time and time-frequency-domain features based on current and voltage measurements. In: 2015 20th international conference on methods and models in automation and robotics (MMAR), Miedzyzdroje, Poland, pp 547–552

    Google Scholar 

  36. Gao W, Hu L, Zhang P (2018) Class-specific mutual information variation for feature selection. Pattern Recognit 79:328–339

    Article  Google Scholar 

  37. Kohavi R, John GH (1997) Wrappers for feature subset selection. Artif Intell 97:273–324

    Article  Google Scholar 

  38. El Aboudi N, Benhlima L (2016) Review on wrapper feature selection approaches. In: 2016 international conference on engineering MIS (ICEMIS), Agadir, Morocco, pp 1–5

    Google Scholar 

  39. Zongker D, Jain A (1996) Algorithms for feature selection: an evaluation. In: Proceedings of 13th international conference on pattern recognition, Vienna, Austria, vol. 2, pp 18–22

    Google Scholar 

  40. Huang C-L, Wang C-J (2006) A GA-based feature selection and parameters optimization for support vector machines. Expert Syst Appl 31:231–240

    Article  Google Scholar 

  41. Cerrada M, Sánchez RV, Cabrera D, Zurita G, Li C (2015) Multi-stage feature selection by using genetic algorithms for fault diagnosis in gearboxes based on vibration signal. Sensors 15:23903–23926

    Article  Google Scholar 

  42. Bradley PS, Mangasarian OL (1998) Feature selection via concave minimization and support vector machines. In: Proceedings of the fifteenth international conference on machine learning, ICML 1998, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc., pp 82–90

    Google Scholar 

  43. Zou H (2006) The adaptive lasso and its oracle properties. J Am Stat Assoc 101:1418–1429

    Article  MathSciNet  Google Scholar 

  44. Tibshirani R (2011) Regression shrinkage and selection via the lasso: a retrospective. J Roy Stat Soc Ser B (Stat Methodol) 73:273–282

    Article  MathSciNet  Google Scholar 

  45. Huang J, Horowitz JL, Ma S (2008) Asymptotic properties of bridge estimators in sparse high-dimensional regression models. Ann Stat 36:587–613. http://arxiv.org/abs/0804.0693

  46. Xiao H, Biggio B, Brown G, Fumera G, Eckert C, Roli F (2015) Is feature selection secure against training data poisoning? In: Proceedings of the 32nd international conference on international conference on machine learning (ICML), ICML 2015, JMLR.org, Lille, France, vol 37, pp 1689–1698

    Google Scholar 

  47. Medjaher K, Zerhouni N, Baklouti J (2013) Data-driven prognostics based on health indicator construction: application to PRONOSTIA’s data. In: 2013 European control conference (ECC), Zurich, Switzerland, pp 1451–1456

    Google Scholar 

  48. Liu K, Gebraeel NZ, Shi J (2013) A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Trans Autom Sci Eng 10:652–664

    Article  Google Scholar 

  49. Pan H, Lü Z, Wang H, Wei H, Chen L (2018) Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine. Energy 160:466–477

    Article  Google Scholar 

  50. Niu G, Jiang J, Youn BD, Pecht M (2018) Autonomous health management for PMSM rail vehicles through demagnetization monitoring and prognosis control. ISA Trans 72:245–255

    Article  Google Scholar 

  51. Atamuradov V, Medjaher K, Camci F, Dersin P, Zerhouni N (2018) Degradation-level assessment and online prognostics for sliding chair failure on point machines. IFAC-PapersOnLine 51:208–213

    Article  Google Scholar 

  52. Sun J, Li C, Liu C, Gong Z, Wang R (2019) A data-driven health indicator extraction method for aircraft air conditioning system health monitoring. Chin J Aeronaut 32:409–416

    Article  Google Scholar 

  53. Malhotra P, et al (2016) Multi-sensor prognostics using an unsupervised health index based on LSTM encoder-decoder. arXiv:1608.06154 [cs]. http://arxiv.org/abs/1608.06154

  54. Gugulothu N, TV V, Malhotra P, Vig L, Agarwal P, Shroff G (2017) Predicting remaining useful life using time series embeddings based on recurrent neural networks. arXiv:1709.01073 [cs]. http://arxiv.org/abs/1709.01073

  55. Guo L, Lei Y, Li N, Yan T, Li N (2018) Machinery health indicator construction based on convolutional neural networks considering trend burr. Neurocomputing 292:142–150

    Article  Google Scholar 

  56. Han T, Liu C, Yang W, Jiang D (2019) Learning transferable features in deep convolutional neural networks for diagnosing unseen machine conditions. ISA Trans 93:341–353

    Article  Google Scholar 

  57. Mao W, He J, Tang J, Li Y (2018) Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network. Adv Mech Eng 10 https://doi.org/10.1177/1687814018817184

  58. Li X, Zhang W, Ding Q (2019) Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliab Eng Syst Saf 182:208–218

    Article  Google Scholar 

  59. Li W, Yue HH, Valle-Cervantes S, Qin SJ (2000) Recursive PCA for adaptive process monitoring. J Process Control 10:471–486

    Article  Google Scholar 

  60. Thieullen A, Ouladsine M, Pinaton J (2012) A survey of health indicators and data-driven prognosis in semiconductor manufacturing process. IFAC Proc Vol 45:19–24

    Article  Google Scholar 

  61. Benkedjouh T, Medjaher K, Zerhouni N, Rechak S (2013) Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Eng Appl Artif Intell 26:1751–1760

    Article  Google Scholar 

  62. Zhang Y, Ye D, Liu Y (2018) Robust locally linear embedding algorithm for machinery fault diagnosis. Neurocomputing 273:323–332

    Article  Google Scholar 

  63. Smart O, Firpi H, Vachtsevanos G (2007) Genetic programming of conventional features to detect seizure precursors. Eng Appl Artif Intell 20:1070–1085

    Article  Google Scholar 

  64. Firpi H, Vachtsevanos G (2008) Genetically programmed-based artificial features extraction applied to fault detection. Eng Appl Artif Intell 21:558–568

    Article  Google Scholar 

  65. Liao L (2014) Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Trans Ind Electron 61:2464–2472

    Article  Google Scholar 

  66. Ramasso E, Saxena A (2014) Review and analysis of algorithmic approaches developed for prognostics on CMAPSS dataset, Technical Report, SGT Inc Moffett Field United States, SGT Inc Moffett Field United States

    Google Scholar 

  67. Cong F, Chen J, Dong G (2012) Spectral kurtosis based on AR model for fault diagnosis and condition monitoring of rolling bearing. J Mech Sci Technol 26:301–306

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Production Engineering Laboratory, INP Toulouse, The National Engineering School of Tarbes, 65000, Tarbes, France

    Khanh T. P. Nguyen

Authors
  1. Khanh T. P. Nguyen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Khanh T. P. Nguyen .

Editor information

Editors and Affiliations

  1. ENSAIT& GEMTEX, University of Lille, Lille, France

    Kim Phuc Tran

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Nguyen, K.T.P. (2022). Feature Engineering and Health Indicator Construction for Fault Detection and Diagnostic. In: Tran, K.P. (eds) Control Charts and Machine Learning for Anomaly Detection in Manufacturing. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-83819-5_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-83819-5_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-83818-8

  • Online ISBN: 978-3-030-83819-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics