access icon openaccess Intelligent fault diagnosis system based on big data

In view of the actual problems existing in life-cycle health monitoring and diagnosis of large complex equipment, the machine-learning algorithm is applied to data mining of the equipment operation big data, the expert knowledge base is established, the diagnosis rules related to the fault are obtained, the intelligent online monitoring and remote diagnosis of the equipment health condition are realised. The system uses uncertain fault prediction method and hybrid intelligent algorithm to discover the hierarchical association between operation feature big data and operation faults, the feature extraction of operation faults, and the intelligent diagnosis of operation faults. It effectively improved the sensitivity, robustness, and accuracy of monitoring and diagnosis. In the cloud service platform based on the Internet of things, the system realises the intelligent fault prediction and diagnosis, establishes a proactive maintenance system, improves the production efficiency, and ensures the production safety.

Inspec keywords: fault diagnosis; maintenance engineering; data mining; feature extraction; computerised monitoring; learning (artificial intelligence); Big Data; condition monitoring; production engineering computing; cloud computing

Other keywords: complex equipment; intelligent online monitoring; intelligent fault prediction; machine-learning algorithm; expert knowledge base; diagnosis rules; actual problems; proactive maintenance system; uncertain fault prediction method; equipment health condition; remote diagnosis; data mining; life-cycle health monitoring; equipment operation big data; hybrid intelligent algorithm; intelligent diagnosis; operation faults; intelligent fault diagnosis system

Subjects: Inspection and quality control; Knowledge engineering techniques; Industrial applications of IT; Other topics in statistics; Production engineering computing; Civil and mechanical engineering computing; Mechanical engineering applications of IT; Power engineering computing; Internet software; Data handling techniques; Maintenance and reliability

References

    1. 1)
      • 15. Huang, M.G., Fan, S.C., Zheng, D.Z., et al: ‘Research progress of multi-sensor data fusion technology’, Transducer Micro Syst. Technol., 2010, 29, (3), pp. 58,12.
    2. 2)
      • 8. Dong, L.Y.: ‘An overview on intelligent fault diagnosis methods’, Programmable Controller Factory Autom., 2010, 2010, (12), pp. 8789.
    3. 3)
      • 14. Sun, Z.D.: ‘Multi-source data fusion oriented Bayesian estimation method research’, J. Qilu Univ. Technol., 2018, 32, (1), pp. 7376.
    4. 4)
      • 21. Zhu, H.B., Yang, L.X., Yu, Q.: ‘Research on the technical ideas and application strategies of the internet of things’, J. Commun., 2010, 31, (11), pp. 29.
    5. 5)
      • 12. Roweis, S.T., Saul, L.K.: ‘Nonlinear dimensionality reduction by locally linear embedding’, Science, 2000, 290, (5500), pp. 23232326.
    6. 6)
      • 10. Lu, Z., Yang, J.: ‘Implementation of aerospace equipment diagnosis system based on fault tree and rule’, J. Comput. Appl., 2015, 35, (S2), pp. 181184.
    7. 7)
      • 9. Liu, Y.M., Su, J., Cao, X.N., et al: ‘Study on fault diagnosis methods for automobile suspensions based on fuzzy mathematics’, J. Jilin Univ. (Eng. Technol. Edition), 2009, 39, (Sup.2), pp. 220224.
    8. 8)
      • 5. Sanz, J., Perera, R., Huerta, C.: ‘Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms’, J. Sound Vib., 2007, 302, (4–5), pp. 981999.
    9. 9)
      • 4. Widodo, A., Yang, B.S.: ‘Support vector machine in machine condition monitoring and fault diagnosis’, Mech. Syst. Signal Process., 2007, 21, (6), pp. 25602574.
    10. 10)
      • 2. Sun, Q., Yue, J.G.: ‘Review on fault prognostic methods based on uncertainty’, Control Decis.., 2007, 29, (5), pp. 769778.
    11. 11)
      • 13. Liu, D.X., Qiu, L.M., Wang, Z.P.: ‘Fault diagnosis technology for complex equipment based on the learning of big operation data and its typical application’, ZTE Technol. J., 2017, 23, (4), pp. 5659.
    12. 12)
      • 20. Kong, X.G., Zhong, F.L., Ma, H.B., et al: ‘Research on hybrid fault diagnosis model in industrial big data environment’. Proc. of Conf. Reliability Technology of the National Machinery Industry, Changzhou, Jiangsu, China, August 2015, pp. 196200.
    13. 13)
      • 18. He, Q., Li, N., Luo, W.J., et al: ‘A survey of machine learning algorithms for big data’, Pattern Recognit. Artif. Intell., 2014, 27, (4), pp. 327336.
    14. 14)
      • 16. Liu, Y., Ding, Y.F.: ‘Review of typical intelligent fault diagnosis methods for wind turbine’, J. Shanghai Dianji Univ., 2017, 20, (6), pp. 353360,372.
    15. 15)
      • 6. Lei, Y.G., He, Z.J.: ‘Advances in applications of hybrid intelligent fault diagnosis and prognosis technique’, J. Vib. Shock, 2011, 30, (9), pp. 129135.
    16. 16)
      • 1. Li, H.W., Yang, D.S., Sun, Y.L., et al: ‘Overview and prospect of intelligent fault diagnosis technology’, Comput. Eng. Design, 2013, 34, (2), pp. 632637.
    17. 17)
      • 17. Bahador, K., Alaa, K., Fakhreddine, O.K., et al: ‘Multisensor data fusion: a review of the state-of-the-art’, Inf. Fusion, 2013, 14, (1), pp. 2844.
    18. 18)
      • 7. Du, J.Q., Zhao, M., Yin, J., et al: ‘Review of fault diagnosis methods for power plant’, Yunnan Electr. Power, 2017, 45, (10), pp. 916, 24.
    19. 19)
      • 3. Jardine, A.K. S., Lin, D., Banjevic, D.: ‘A review on machinery diagnostics and prognostics implementing condition-based maintenance’, Mech. Syst. Signal Process., 2006, 20, (7), pp. 14831510.
    20. 20)
      • 19. Pan, W., Li, Z.H.: ‘Development of parallel computing models in the big data era’, J. East China Normal Univ. (Natural Sci.), 2014, 177, (5), pp. 4354.
    21. 21)
      • 11. Peng, Y., Liu, D.T., Peng, X.Y.: ‘A review: prognostics and health management’, J. Electron. Meas. Instrum., 2010, 24, (1), pp. 19.
http://iet.metastore.ingenta.com/content/journals/10.1049/joe.2018.9162
Loading

Related content

content/journals/10.1049/joe.2018.9162
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading