Advertisement

A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm

  • Wu Deng
  • Rui Yao
  • Huimin Zhao
  • Xinhua Yang
  • Guangyu Li
Methodologies and Application

Abstract

Aiming at the problem that the most existing fault diagnosis methods could not effectively recognize the early faults in the rotating machinery, the empirical mode decomposition, fuzzy information entropy, improved particle swarm optimization algorithm and least squares support vector machines are introduced into the fault diagnosis to propose a novel intelligent diagnosis method, which is applied to diagnose the faults of the motor bearing in this paper. In the proposed method, the vibration signal is decomposed into a set of intrinsic mode functions (IMFs) by using empirical mode decomposition method. The fuzzy information entropy values of IMFs are calculated to reveal the intrinsic characteristics of the vibration signal and considered as feature vectors. Then the diversity mutation strategy, neighborhood mutation strategy, learning factor strategy and inertia weight strategy for basic particle swarm optimization (PSO) algorithm are used to propose an improved PSO algorithm. The improved PSO algorithm is used to optimize the parameters of least squares support vector machines (LS-SVM) in order to construct an optimal LS-SVM classifier, which is used to classify the fault. Finally, the proposed fault diagnosis method is fully evaluated by experiments and comparative studies for motor bearing. The experiment results indicate that the fuzzy information entropy can accurately and more completely extract the characteristics of the vibration signal. The improved PSO algorithm can effectively improve the classification accuracy of LS-SVM, and the proposed fault diagnosis method outperforms the other mentioned methods in this paper and published in the literature. It provides a new method for fault diagnosis of rotating machinery.

Keywords

Intelligent diagnosis Feature extraction Fuzzy information entropy Multi-strategy Particle swarm optimization Least squares support vector machines Combinatorial optimization 

Notes

Acknowledgements

The authors would like to thank all the reviewers for their constructive comments. This research was supported by the National Natural Science Foundation of China (51475065, 51605068, 61771087, U1433124), Open Project Program of Sichuan Provincial Key Lab of Process Equipment and Control (GK201613), Open Project Program of the Traction Power State Key Laboratory of Southwest Jiaotong University (TPL1705), Natural Science Foundation of Liaoning Province (2015020013, 20170540126, 20170540125), and Science and Technology Project of Liaoning Provincial Department of Education (JDL2016030). The program for the initialization, study, training, and simulation of the proposed algorithm in this article was written with the toolbox of MATLAB 2010b produced by the MathWorks, Inc.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflicts of interest.

Ethical standard

This article does not contain any studies with human participants performed by any of the authors.

References

  1. Ahmadi MA (2011) Prediction of asphaltene precipitation using artificial neural network optimized by imperialist competitive algorithm. J Petrol Explor Prod Technol 1(2–4):99–106CrossRefGoogle Scholar
  2. Ahmadi MA, Bahadori A (2015) A LSSVM approach for determining well placement and conning phenomena in horizontal wells. Fuel 153:276–283CrossRefGoogle Scholar
  3. Ahmadi MA, Shadizadeh SR (2012) New approach for prediction of asphaltene precipitation due to natural depletion by using evolutionary algorithm concept. Fuel 102:716–723CrossRefGoogle Scholar
  4. Ahmadi MA, Lee M, Bahadori A (2015) Prediction of a solid desiccant dehydrator performance using least squares support vector machines algorithm. J Taiwan Inst Chem Eng 50:115–122CrossRefGoogle Scholar
  5. Ahmadi MA, Hasanvand MZ, Bahadori A (2015) A LSSVM approach to predict temperature drop accompanying a given pressure drop for the natural gas production and processing Systems. Int J Ambient Energy 38:122–129.  https://doi.org/10.1080/01430750.2015.1055515 CrossRefGoogle Scholar
  6. Bae YC (2016) An improved measurement method for the strength of radiation of reflective beam in an industrial optical sensor based on laser displacement meter. Sensors (Switzerland) 16(5):23CrossRefGoogle Scholar
  7. Basir O, Yuan XD (2007) Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory. Inf Fusion 8(4):379–386CrossRefGoogle Scholar
  8. Bin GF, Gao JJ, Li XJ, Dhillon BS (2012) Early fault diagnosis of rotating machinery based on wavelet packets-Empirical mode decomposition feature extraction and neural network. Mech Syst Signal Process 27(1):696–711CrossRefGoogle Scholar
  9. Chandra NH, Sekhar AS (2016) Fault detection in rotor bearing systems using time frequency techniques. Mech Syst Signal Process 72–73:105–133CrossRefGoogle Scholar
  10. Chen FF, Tang BP, Song T, Li L (2014) Multi-fault diagnosis study on roller bearing based on multi-kernel support vector machine with chaotic particle swarm optimization. Measurement 47(1):576–590CrossRefGoogle Scholar
  11. Chen BJ, Yang JH, Jeon B, Zhang XP (2017) Kernel quaternion principal component analysis and its application in RGB-D object recognition. Neurocomputing 266:293–303CrossRefGoogle Scholar
  12. Chiang LH, Kotanchek ME, Kordon AK (2004) Fault diagnosis based on fisher discriminant analysis and support vector machines. Comput Chem Eng 28(8):1389–1401CrossRefGoogle Scholar
  13. Chu DL, He Q, Mao XH (2016) Rolling bearing fault diagnosis by a novel fruit fly optimization algorithm optimized support vector machine. J Vibroeng 18(1):151–164Google Scholar
  14. Deng W, Zhao HM, Yang XH, Xiong JX, Sun M, Li B (2017) Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment. Appl Soft Comput 59:288–302CrossRefGoogle Scholar
  15. Deng W, Zhao HM, Zou L, Li GY, Yang XH, Wu DQ (2017) A novel collaborative optimization algorithm in solving complex optimization problems. Soft Comput 21(15):4387–4398CrossRefGoogle Scholar
  16. Fei SW, Zhang XB (2009) Fault diagnosis of power transformer based on support vector machine with genetic algorithm. Expert Syst Appl 36(8):11352–11357CrossRefGoogle Scholar
  17. Fu ZJ, Wu XL, Guan CW, Sun XM, Ren K (2016) Toward efficient multi-keyword fuzzy search over encrypted outsourced data with accuracy improvement. IEEE Trans Inf Forensic Secur 11(12):2706–2716CrossRefGoogle Scholar
  18. Gu B, Sheng VS (2017) A robust regularization path algorithm for \(\nu \)-support vector classification. IEEE Trans Neural Netw Learn Syst 28(5):1241–1248CrossRefGoogle Scholar
  19. Gu B, Sheng VS, Tay KY, Romano W, Li S (2015) Incremental support vector learning for ordinal regression. IEEE Trans Neural Netw Learn Syst 26(7):1403–1416MathSciNetCrossRefGoogle Scholar
  20. Gu B, Sun XM, Sheng VS (2017) Structural minimax probability machine. IEEE Trans Neural Netw Learn Syst 28(7):1646–1656MathSciNetCrossRefGoogle Scholar
  21. Gustafsson O, Tallian T (1962) Detection of in assembled rolling element bearings. ASLE Trans 5(1):197–209CrossRefGoogle Scholar
  22. Hu Q, He ZJ, Zhang ZS, Zi Y (2007) Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mech Syst Signal Process 21(2):688–705CrossRefGoogle Scholar
  23. Hu HX, Tang B, Gong XJ, Wei W, Wang H (2017) Intelligent fault diagnosis of the High-speed train with big data based on deep neural networks. IEEE Trans Ind Inf 13(4):2106–2116CrossRefGoogle Scholar
  24. Jaouher BA, Nader F, Lotfi S, Chebel-Morello B, Fnaiech F (2015) Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl Acoust 89(3):16–27Google Scholar
  25. Jung YO, Bae YC (2015) Analysis of fault diagnosis for current and vibration signals in pumps and motors using a reconstructed phase portrait. Int J Fuzzy Logic Intell Syst 15(3):166–171CrossRefGoogle Scholar
  26. Kadri O, Mouss LH, Mouss MD (2012) Fault diagnosis of rotary kiln using SVM and binary ACO. J Mech Sci Technol 26(2):601–608CrossRefGoogle Scholar
  27. Kankar PK, Sharma SC, Harsha SP (2011) Rolling element bearing fault diagnosis using wavelet transform. Neurocomputing 74(10):1638–1645CrossRefGoogle Scholar
  28. Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using continuous wavelet transform. Appl Soft Comput 11(2):2300–2312CrossRefGoogle Scholar
  29. Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, IEEE Press, Piscataway, 1942–1948Google Scholar
  30. Kong Y, Zhang MJ, Ye DY (2016) A belief propagation-based method for task allocation in open and dynamic cloud environments. Knowl based Syst 115:123–132CrossRefGoogle Scholar
  31. Lee CJ, Lee G, Han CH, Yoon ES (2006) A hybrid model for fault diagnosis using model based approaches and support vector machine. J Chem Eng Japan 39(10):1085–1095CrossRefGoogle Scholar
  32. Lee JM, Qin SJ, Lee IB (2010) Fault detection and diagnosis based on modified independent component analysis. AICHE J 52(10):3501–3514CrossRefGoogle Scholar
  33. Lei YG, Lin J, He ZJ, Zuo MJ (2013) A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Signal Process 35(1–2):108–126CrossRefGoogle Scholar
  34. Li B, Chow MY, Tipsuwan Y (2000) Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans Ind Electron 47(5):1060–1069CrossRefGoogle Scholar
  35. Li YJ, Zhang WH, Xiong Q, Luo DB, Mei GM, Zhang T (2017) A rolling bearing fault diagnosis strategy based on improved multiscale permutation entropy and least squares SVM. J Mech Sci Technol 31(6):2711–2722CrossRefGoogle Scholar
  36. Lin J, Qu LS (2000) Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. J Sound Vib 234(1):135–148CrossRefGoogle Scholar
  37. Liu B, Riemenschneider S, Xun Y (2006) Gearbox fault diagnosis using empirical mode decomposition and Hilbert spectrum. Mech Syst Signal Process 20(3):718–734CrossRefGoogle Scholar
  38. Liu Q, Cai WD, Shen J, Fu ZJ, Liu XD, Linge N (2016) A speculative approach to spatial-temporal efficiency with multi-objective optimization in a heterogeneous cloud environment. Secur Commun Netw 9(17):4002–4012CrossRefGoogle Scholar
  39. Lou XS, Loparo KA (2004) Bearing fault diagnosis based on wavelet transform and fuzzy inference. Mech Syst Signal Process 18(5):1077–1095CrossRefGoogle Scholar
  40. Ma TH, Wang Y, Tang ML, Cao J, Tian Y, Al-Dhelaan A, Al-Rodhaan M (2016) LED: a fast overlapping communities detection algorithm based on structural clustering. Neurocomputing 207:488–500CrossRefGoogle Scholar
  41. Nandi S, Toliyat HA, Li XD (2005) Condition monitoring and fault diagnosis of electrical motors—a review. IEEE Trans Energy Convers 20(4):719–729CrossRefGoogle Scholar
  42. Oliveira JCM, Pontes KV, Sartori I (2017) Fault detection and diagnosis in dynamic systems using weightless neural networks. Expert Syst Appl 84:200–219CrossRefGoogle Scholar
  43. Pan ZQ, Zhang Y, Kwong S (2015) Efficient motion and disparity estimation optimization for low complexity multiview video coding. IEEE Trans Broadcast 61(2):166–176CrossRefGoogle Scholar
  44. Pandya DH, Upadhyay SH, Harsha SP (2014) Fault diagnosis of rolling element bearing by using multinomial logistic regression and wavelet packet transform. Soft Comput 18(2):255–266CrossRefGoogle Scholar
  45. Purushotham V, Narayanan S, Prasad S (2005) Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition. Ndt E Int 38(8):654–664CrossRefGoogle Scholar
  46. Rai VK, Mohanty AR (2007) Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert-Huang transform. Mech Syst Signal Process 21(6):2607–2615CrossRefGoogle Scholar
  47. Rodriguez Ramos A, Llanes-Santiago O, Bernal de lazaro JM (2017) A novel fault diagnosis scheme applying fuzzy clustering algorithms. Appl Soft Comput 58:605–619CrossRefGoogle Scholar
  48. Rong H, Ma TH, Tang ML, Cao J (2017) A novel subgraph K+ -isomorphism method in social network based on graph similarity detection. Soft Comput.  https://doi.org/10.1007/s00500-017-2513-y
  49. Rubini R, Meneghetti U (2001) Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mech Syst Signal Process 15(2):287–302CrossRefGoogle Scholar
  50. Shen ZJ, Chen XF, Zhang XL, He Z (2012) A novel intelligent gear fault diagnosis model based on EMD and multi-class TSVM. Measurement 45(1):30–40CrossRefGoogle Scholar
  51. Sun YJ, Gu FH (2017) Compressive sensing of piezoelectric sensor response signal for phased array structural health monitoring. Int J Sensor Netw 23(4):258–264CrossRefGoogle Scholar
  52. Sun J, Qin SY, Song YH (2004) Fault diagnosis of electric power systems based on fuzzy petri nets. IEEE Trans Power Syst 19(4):2053–2059CrossRefGoogle Scholar
  53. Van TT, AlThobiani F, Ball A (2013) An application to transient current signal based induction motor fault diagnosis of Fourier–Bessel expansion and simplified fuzzy ARTMA. Expert Syst Appl 40(13):5372–5384CrossRefGoogle Scholar
  54. Vokelj M, Zupan S, Prebil I (2016) EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis. J Sound Vib 370:394–423CrossRefGoogle Scholar
  55. Wang L, Niu Q, Fei MR (2008) A novel quantum ant colony optimization algorithm and its application to fault diagnosis. Trans Inst Meas Control 30(3–4):313–329CrossRefGoogle Scholar
  56. Wang BW, Gu XD, Ma L, Yan SS (2017) Temperature error correction based on BP neural network in meteorological WSN. Int J Sensor Netw 23(4):265–278CrossRefGoogle Scholar
  57. Wang JW, Lian SG, Shi YQ (2017) Hybrid multiplicative multi-watermarking in DWT domain. Multidimens Syst Signal Process 28(2):617–636CrossRefGoogle Scholar
  58. Widodo A, Yang BS (2007) Support vector machine in machine condition monitoring and fault diagnosis. Mech Syst Signal Process 21(6):2560–2574CrossRefGoogle Scholar
  59. Wu Q, Law R, Wu SY (2011) Fault diagnosis of car assembly line based on fuzzy wavelet kernel support vector classifier machine and modified genetic algorithm. Expert Syst Appl 38(8):9096–9104CrossRefGoogle Scholar
  60. Xiong LZ, Xu ZQ, Shi YQ (2017) An integer wavelet transform based scheme for reversible data hiding in encrypted images. Multidimens Syst Signal Process.  https://doi.org/10.1007/s11045-017-0497-5
  61. Xue Y, Jiang JM, Zhao BP, Ma TH (2017) A self-adaptive artificial bee colony algorithm based on global best for global optimization. Soft Comput.  https://doi.org/10.1007/s00500-017-2547-1
  62. Yu DJ, Cheng JS, Yang Y (2005) Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings. Mech Syst Signal Process 19(2):259–270CrossRefGoogle Scholar
  63. Yu Y, Yu DJ, Cheng JS (2006) A roller bearing fault diagnosis method based on EMD energy entropy and ANN. J Sound Vib 294(1–2):269–277CrossRefGoogle Scholar
  64. Yuan CS, Sun XM, LV R (2016) Fingerprint liveness detection based on multi-scale LPQ and PCA. China Commun 13(7):60–65CrossRefGoogle Scholar
  65. Zhang XL, Chen W, Wang BJ, Chen F (2015) Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization. Neurocomputing 167:260–279CrossRefGoogle Scholar
  66. Zhang YH, Sun XM, Wang BW (2016) Efficient algorithm for K-barrier coverage based on integer linear programming. China Commun 13:16–23CrossRefGoogle Scholar
  67. Zhang J, Tang J, Wang TB, Chen F (2017) Energy-efficient data-gathering rendezvous algorithms with mobile sinks for wireless sensor networks. Int J Sensor Netw 23(4):248–257CrossRefGoogle Scholar
  68. Zhao CL, Sun XB, Sun SL, Jiang T (2011) Fault diagnosis of sensor by chaos particle swarm optimization algorithm and support vector machine. Expert Syst Appl 38(8):9908–9912CrossRefGoogle Scholar
  69. Zhao HM, Sun M, Deng W, Yang XH (2017) A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy 19(1):14CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  • Wu Deng
    • 1
    • 2
    • 3
    • 4
    • 5
  • Rui Yao
    • 2
  • Huimin Zhao
    • 1
    • 3
    • 4
    • 5
  • Xinhua Yang
    • 1
    • 5
  • Guangyu Li
    • 1
  1. 1.Software InstituteDalian Jiaotong UniversityDalianChina
  2. 2.School of Electronics and Information EngineeringDalian Jiaotong UniversityDalianChina
  3. 3.Sichuan Provincial Key Lab of Process Equipment and Control (Sichuan University of Science and Engineering)ZigongChina
  4. 4.Traction Power State Key Laboratory of Southwest Jiaotong UniversityChengduChina
  5. 5.Liaoning Key Laboratory of Welding and Reliability of Rail Transportation EquipmentDalian Jiaotong UniversityDalianChina

Personalised recommendations