Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Intelligent Fault Diagnosis for Industrial Big Data

  • 791 Accesses

  • 7 Citations

Abstract

With the rapid development of the Internet of Things (IoT), industrial big data can now be collected through many different sources, such as multimedia. Intelligent fault diagnosis is recognized as an important and promising approach in using these data, because it can provide accurate diagnosis and adjust to different deployed environments. In this study, intelligent fault diagnosis approaches for real machine data sets are comprehensively investigated. First, support vector machine (SVM) and popular neural network approaches are implemented and compared. Results show that while the neural network-based method is very efficient in many high-dimensional applications, such as video, SVM performs well enough for intelligent fault diagnosis. Second, the relation between the number of samples and the efficiency of diagnosis are studied, Findings indicate that a small number of samples can produce an optimal result. Furthermore, accuracy does not increase with training data because of the inherent fuzziness of machine monitoring data. Finally, the accuracy of conditioning and diagnosis is demonstrated for several kinds of machine data.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
Figure 10
Figure 11
Figure 12
Figure 13
Figure 14

References

  1. 1.

    Wu, X., Zhu, X., GQ, W., et al. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107.

  2. 2.

    Blat, J., et al. (2016). Big data analysis for media production. Proceedings of the IEEE, 104(11), 2085–2113.

  3. 3.

    Zhou, P., et al. (2016). Differentially private online learning for cloud-based video recommendation with multimedia big data in social networks. IEEE Transactions on Multimedia, 18(6), 1217–1229.

  4. 4.

    Gai, K., Qiu, M., Zhao, H., et al. (2016). Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing. Journal of Network and Computer Applications, 59(C), 46–54.

  5. 5.

    Gai, K., Qiu, M., & Zhao, H. (2017). Energy-aware task assignment for mobile cyber-enabled applications in heterogeneous cloud computing. Journal of Parallel & Distributed Computing, 111:126–135.

  6. 6.

    Gai, K., Qiu, M., Tao, L., et al. (2016). Intrusion detection techniques for mobile cloud computing in heterogeneous 5G. Security and Communication Networks, 9(16), 3049–3058.

  7. 7.

    Gai, K., Qiu, M., Ming, Z., et al. (2017). Spoofing-jamming attack strategy using optimal power distributions in wireless smart grid networks. IEEE Transactions on Smart Grid, 8(5), 2431–2439.

  8. 8.

    Gai, K., Qiu, L., Chen, M., et al. (2017). SA-EAST: security-aware efficient data transmission for ITS in mobile heterogeneous cloud computing. ACM Transactions on Embedded Computing Systems, 16(2), 1–22.

  9. 9.

    Qiu M, Chen Z, Ming Z, et al. (2014) Energy-aware data allocation with hybrid memory for mobile cloud systems. IEEE Systems Journal, (99), 1–10.

  10. 10.

    Qiu, M., Zhong, M., Li, J., et al. (2015). Phase-change memory optimization for green cloud with genetic algorithm. IEEE Transactions on Computers, 64(12), 3528–3540.

  11. 11.

    Li, Y., Dai, W., Ming, Z., et al. (2016). Privacy protection for preventing data over-collection in Smart City. IEEE Transactions on Computers, 65(5), 1339–1350.

  12. 12.

    Zhu, X., Qin, X., & Qiu, M. (2011). QoS-aware fault-tolerant scheduling for real-time tasks on heterogeneous clusters. IEEE Transactions on Computers, 60(6), 800–812.

  13. 13.

    Nandi, S., Toliyat, H. A., & Li, X. (2005). Condition monitoring and fault diagnosis of electrical motors—A review. IEEE Transactions on Energy Conversion, 20(4), 719–729.

  14. 14.

    Vas, P. (1993). Parameter estimation, condition monitoring, and diagnosis of electrical machines. Oxford: Clarendon.

  15. 15.

    Kim, K., Parlos, A. G., & Mohan Bharadwaj, R. (2003). Sensorless fault diagnosis of induction motors. IEEE Transactions on Industrial Electronics, 50(5), 1038–1051.

  16. 16.

    Kim, K., & Parlos, A. G. (2002). Induction motor fault diagnosis based on neuropredictors and wavelet signal processing. IEEE/ASME Transactions on Mechatronics, 7(2), 201–219.

  17. 17.

    Su, H., & Chong, K. T. (2007). Induction machine condition monitoring using neural network modeling. IEEE Transactions on Industrial Electronics, 54(1), 241–249.

  18. 18.

    Chen, Z., & Li, W. (2017). Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Transactions on Instrumentation and Measurement, 66(7), 1693–1702.

  19. 19.

    Lei, Y., et al. (2016). An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Transactions on Industrial Electronics, 63(5), 3137–3147.

  20. 20.

    Seshadrinath, J., Singh, B., & Panigrahi, B. K. (2014). Vibration analysis based Interturn fault diagnosis in induction machines. IEEE Transactions on Industrial Informatics, 10(1), 340–350.

  21. 21.

    Liu, R., Meng, G., Yang, B., et al. (2017). Dislocated time series convolutional neural architecture: An intelligent fault diagnosis approach for electric machine. IEEE Transactions on Industrial Informatics, 13(3), 1310–1320.

  22. 22.

    Prieto, M. D., et al. (2013). Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Transactions on Industrial Electronics, 60(8), 3398–3407.

  23. 23.

    Shatnawi, Y., & Alkhassaweneh, M. (2014). Fault diagnosis in internal combustion engines using extension neural network. IEEE Transactions on Industrial Electronics, 61(3), 1434–1443.

  24. 24.

    Malik, H. K., & Mishra, S. (2016). Artificial neural network and empirical mode decomposition based imbalance fault diagnosis of wind turbine using TurbSim, FAST and Simulink. IET Renewable Power Generation, 11(6), 889–902.

  25. 25.

    Yang, Y., Yu, D., & Cheng, J. (2007). A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement, 40(9), 943–950.

  26. 26.

    Soualhi, A., Medjaher, K., & Zerhouni, N. (2014). Bearing health monitoring based on Hilbert–Huang transform, support vector machine, and regression. IEEE Transactions on Instrumentation and Measurement, 64(1), 52–62.

  27. 27.

    Ren, L., et al. (2016). Fault diagnosis using a joint model based on sparse representation and SVM. IEEE Transactions on Instrumentation and Measurement, 65(10), 2313–2320.

  28. 28.

    Kang, M., Kim, J., & Kim, J. (2015). An FPGA-based multicore system for real-time bearing fault diagnosis using ultrasampling rate AE signals. IEEE Transactions on Industrial Electronics, 62(4), 2319–2329.

  29. 29.

    Zhao, Z., Xin, H., Ren, Y., et al. (2010). Application and comparison of BP neural network algorithm in MATLAB, in Measuring Technology And Mechatronics Automation (ICMTMA), International Conference on. IEEE, pp. 590–593.

  30. 30.

    Chang, C. C., & Lin, C. J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2(3), 27.

  31. 31.

    Scholkopf, B., & Smola, A. J. Learning with kernels: support vector machines, regularization, optimization, and beyond (pp. 405–426). Cambridge: MIT press.

  32. 32.

    Zhang, J., & Zhang, W. (2013). Intelligent fault diagnosis and prognosis for equipment (pp. 162–178). China: National Defense Industry.

Download references

Acknowledgments

This work is supported by National Key R&D Program of China (2017YFB0404201).

Author information

Correspondence to Sile Ma.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Si, J., Li, Y. & Ma, S. Intelligent Fault Diagnosis for Industrial Big Data. J Sign Process Syst 90, 1221–1233 (2018). https://doi.org/10.1007/s11265-017-1316-9

Download citation

Keywords

  • Big data
  • Fault diagnosis
  • Internet of things
  • SVM
  • Neural network