Unsupervised and non-parametric learning-based anomaly detection system using vibration sensor data

  • Seyoung Park
  • Jaewoong Kang
  • Jongmo Kim
  • Seongil Lee
  • Mye Sohn
Article
  • 23 Downloads

Abstract

In this paper, we propose an anomaly detection system of machines using a hybrid learning mechanism that combines two kinds of machine learning approaches, namely unsupervised and non-parametric learning. To do so, we used vibration data, which is known to be suitable for anomaly detection in machines during operation. Furthermore, in order to take into account various characteristics of abnormal data such as scarcity and diversity, we propose a novel method that can detect anomalous behaviors using normal patterns instead of abnormal patterns from the machines. That is, we first perform a machine learning of the normal patterns of the machines during operation, and if any of the operation patterns deviates from the normal pattern, we identify that pattern as abnormal. A key characteristic of our system is that it does not use any prior information such as predefined data labels or data distributions to learn the normal operation patterns. To demonstrate the superiority of our system, we constructed a test bed consisting of a washing machine and a 3-axis accelerometer. We also demonstrated that our system can improve the accuracy of anomaly detection for the machines compared to other approaches.

Keywords

Anomaly detection Unsupervised and non-parametric machine learning Pattern recognition Non-stationary Markov chain Vibration data 

Notes

Acknowledgements

This research was partially supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF-2016 R1D1A1B03932110) and partially supported by the IT R&D program of KEIT (No. 1005-0810, Development of Disability Independent Accessibility Enhancement Technology for Input and Abnormality of Home Appliances).

References

  1. 1.
    Abdel-Sayed M, Duclos D, Faÿ G, Lacaille J, Mougeot M (2016) NMF-based decomposition for anomaly detection applied to vibration analysis. International Journal of Condition Monitoring 6(3):73–81CrossRefGoogle Scholar
  2. 2.
    Ahmad S, Lavin A, Purdy S, Agha Z (2017) Unsupervised real-time anomaly detection for streaming data. Neurocomputing 262:134–147CrossRefGoogle Scholar
  3. 3.
    Cao H, Wu S, Zhou Z, Lin CC, Yang CY, Lee ST, Wu CT (2016) “A fall detection method based on acceleration data and hidden Markov model,” 2016 I.E. International Conference on Signal and Image Processing (ICSIP), 2016Google Scholar
  4. 4.
    Gao Y, Yang T, Xu M, Xing N (2012) “An Unsupervised Anomaly Detection Approach for Spacecraft Based on Normal Behavior Clustering,” 2012 Fifth International Conference on Intelligent Computation Technology and AutomationGoogle Scholar
  5. 5.
    Goh J, Adepu S, Tan M, Lee ZS (2017) “Anomaly Detection in Cyber Physical Systems Using Recurrent Neural Networks,” 2017 I.E. 18th International Symposium on High Assurance Systems Engineering (HASE)Google Scholar
  6. 6.
    Goldstein M, Uchida S (2016) A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. PLoS One 11(4):e0152173CrossRefGoogle Scholar
  7. 7.
    Kim YJ, Kang BN, Kim D (2015) “Hidden Markov Model Ensemble for Activity Recognition Using Tri-Axis Accelerometer,” 2015 I.E. International Conference on Systems, Man, and Cybernetics,Google Scholar
  8. 8.
    Lee SK, White PR (1998) The enhancement of impulsive noise and vibration signals for fault detection in rotating and reciprocating machinery. J Sound Vib 217(3):485–505CrossRefGoogle Scholar
  9. 9.
    Li W, Monti A, Ponci F (2014) Fault Detection and Classification in Medium Voltage DC Shipboard Power Systems With Wavelets and Artificial Neural Networks. IEEE Trans Instrum Meas 63(11):2651–2665CrossRefGoogle Scholar
  10. 10.
    Li L, Hansman RJ, Palacios R, Welsch R (2016) Anomaly detection via a Gaussian Mixture Model for flight operation and safety monitoring. Transportation Research Part C: Emerging Technologies 64:45–57CrossRefGoogle Scholar
  11. 11.
    Li C, Sánchez R-V, Zurita G, Cerrada M, Cabrera D (2016) Fault Diagnosis for Rotating Machinery Using Vibration Measurement Deep Statistical Feature Learning. Sensors 16(12):895CrossRefGoogle Scholar
  12. 12.
    Liu J, Seraoui R, Vitelli V, Zio E (2013) Nuclear power plant components condition monitoring by probabilistic support vector machine. Ann Nucl Energy 56:23–33CrossRefGoogle Scholar
  13. 13.
    Liu Q, Klucik R, Chen C, Grant G, Gallaher D, Lv Q, Shang L (2017) Unsupervised detection of contextual anomaly in remotely sensed data. Remote Sens Environ 202:75–87CrossRefGoogle Scholar
  14. 14.
    Martí L, Sanchez-Pi N, Molina J, Garcia A (2015) Anomaly Detection Based on Sensor Data in Petroleum Industry Applications. Sensors 15(12):2774–2797CrossRefGoogle Scholar
  15. 15.
    Morrow A, Baseman E, Blanchard S (2016) “Ranking Anomalous High Performance Computing Sensor Data Using Unsupervised Clustering,” 2016 International Conference on Computational Science and Computational Intelligence (CSCI)Google Scholar
  16. 16.
    Nandi AK, Liu C, Wong MD (2013) Intelligent vibration signal processing for condition monitoring. In Proceedings of the International Conference Surveillance (Vol. 7, pp. 1–15)Google Scholar
  17. 17.
    Olsen RC (2016) Remote Sensing from Air and Space. SPIE, WashingtonGoogle Scholar
  18. 18.
    Peerbhay KY, Mutanga O, Ismail R (2015) Random Forests Unsupervised Classification: The Detection and Mapping of <italic>Solanum mauritianum</italic> Infestations in Plantation Forestry Using Hyperspectral Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8(6):3107–3122CrossRefGoogle Scholar
  19. 19.
    Prieto MD, Cirrincione G, Espinosa AG, Ortega JA, Henao H (2013) Bearing Fault Detection by a Novel Condition-Monitoring Scheme Based on Statistical-Time Features and Neural Networks. IEEE Trans Ind Electron 60(8):3398–3407CrossRefGoogle Scholar
  20. 20.
    Rassam MA, Maarof MA, Zainal A (2014) Adaptive and online data anomaly detection for wireless sensor systems. Knowl-Based Syst 60:44–57CrossRefGoogle Scholar
  21. 21.
    Ross TJ (2010) Fuzzy Logic with Engineering Applications. Wiley, New JerseyGoogle Scholar
  22. 22.
    Shen C, Wang D, Kong F, Tse PW (2013) Fault diagnosis of rotating machinery based on the statistical parameters of wavelet packet paving and a generic support vector regressive classifier. Measurement 46(4):1551–1564CrossRefGoogle Scholar
  23. 23.
    Song J, Takakura H, Okabe Y, Nakao K (2013) Toward a more practical unsupervised anomaly detection system. Inf Sci 231:4–14CrossRefGoogle Scholar
  24. 24.
    Sun C, Zhang Z, He Z (2011) Research on bearing life prediction based on support vector machine and its application. J Phys Conf Ser 305:012028CrossRefGoogle Scholar
  25. 25.
    Tong L, Song Q, Ge Y, Liu M (2013) HMM-Based Human Fall Detection and Prediction Method Using Tri-Axial Accelerometer. IEEE Sensors J 13(5):1849–1856CrossRefGoogle Scholar
  26. 26.
    Wang G, Yin S (2014) Data-driven fault diagnosis for an automobile suspension system by using a clustering based method. Journal of the Franklin Institute 351(6):3231–3244MathSciNetCrossRefMATHGoogle Scholar
  27. 27.
    Widodo A, Yang B-S (2011) Machine health prognostics using survival probability and support vector machine. Expert Syst Appl 38(7):8430–8437CrossRefGoogle Scholar
  28. 28.
    Wijayasekara D, Linda O, Manic M, Rieger C (2014) Mining Building Energy Management System Data Using Fuzzy Anomaly Detection and Linguistic Descriptions. IEEE Transactions on Industrial Informatics 10(3):1829–1840CrossRefGoogle Scholar
  29. 29.
    Zarei J, Tajeddini MA, Karimi HR (2014) Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics 24(2):151–157CrossRefGoogle Scholar
  30. 30.
    Zhang F, Liu Y, Chen C, Li Y-F, Huang H-Z (2014) Fault diagnosis of rotating machinery based on kernel density estimation and Kullback-Leibler divergence. J Mech Sci Technol 28(11):4441–4454CrossRefGoogle Scholar
  31. 31.
    Zhang Y, Bingham C, Gallimore M, Cox D (2015) “Novelty detection based on extensions of GMMs for industrial gas turbines,” 2015 I.E. International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)Google Scholar
  32. 32.
    Zhang L, Lin J, Karim R (2018) Adaptive kernel density-based anomaly detection for nonlinear systems. Knowl-Based Syst 139:50–63CrossRefGoogle Scholar
  33. 33.
    Zhou Y, Cheng Z, Jing L, Wang J, Huang T (2013) Pre-classification based hidden Markov model for quick and accurate gesture recognition using a finger-worn device. Appl Intell 40(4):613–622CrossRefGoogle Scholar
  34. 34.
    Zimroz R, Bartelmus W, Barszcz T, Urbanek J (2014) Diagnostics of bearings in presence of strong operating conditions non-stationarity—A procedure of load-dependent features processing with application to wind turbine bearings. Mech Syst Signal Process 46(1):16–27CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Industrial EngineeringSungkyunkwan UniversitySuwonSouth Korea

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