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Abstract

The slewing bearing with heavy-load and low-speed, as the key part of large rotating machine, has been widely applied in engineering. This paper presents an improved method to recognize the life state of slewing bearing, which guarantees the machine work efficiently and stably. Firstly, point density function with fuzzy C means (D-FCM) was proposed as an unsupervised method to recognize the life state of the slewing bearing. Then, an experiment on the full life test of slewing bearing was conducted based on a home-made test platform to demonstrate the effectiveness of the proposed method. Finally, the principal component analysis (PCA) was used as a supervised method for comparison. The results indicate D-FCM in unsupervised classification can recognize normal state, degeneration state and failure state of slewing bearing, which is more accurate and clear than traditional FCM and PCA, and lays the foundation of real-time maintenance.

Keywords

Slewing bearing life state recognition residual life prediction fuzzy c-means principal component analysis

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References

Caesarendra

Kosasih

Tieu

A K

et al . (2014) Circular domain features based condition monitoring for low speed slewing bearing. Mechanical Systems and Signal Processing 45(1): 114–138.

Chiang

Braatz

Russell

(2000) Fault Detection and Diagnosis in Industrial Systems. London: Springer Science & Business Media.

Ding

Zhang

Ding

et al . (2010) On the application of PCA technique to fault diagnosis. Tsinghua Science & Technology 15(02): 138–144.

Liu

et al . (2015) Rolling bearing diagnosing method based on time domain analysis and adaptive fuzzy C-means clustering. Shock and Vibration 2016(1): 1–8.

Ghods

Lee

(2016) Probabilistic frequency-domain discrete wavelet transform for better detection of bearing faults in induction motors. Neurocomputing 188: 206–216.

Hong

Zhou

Zio

et al . (2014) Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method. Digital Signal Processing 27: 159–166.

Jiang

Zhu

et al . (2014) Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis. Journal of Sound and Vibration 333(14): 3321–3331.

Kedadouche

Liu

(2016) A new approach based on OMA-empirical wavelet transforms for bearing fault diagnosis. Measurement 90: 292–308.

Leski

(2016) Fuzzy c-ordered-means clustering. Fuzzy Sets and Systems 286: 114–133.

10.

Liu

Peng

et al . (2016) Overview of principal component analysis algorithm. Optik 127(9): 3935–3944.

11.

Chen

Hong

et al . (2016) Degradation trend estimation of slewing bearing based on LSSVM model. Mechanical Systems and Signal Processing 76: 353–366.

12.

Meng

Chen

Wang

et al . (2013) Research on accelerated fatigue life test of slewing bearings used in wind turbines. Modular Machine Tool & Automatic Manufacturing Technique 12: 83–85 (in Chinese).

13.

Moura

EPD

Souto

Silva

(2011) Evaluation of principal component analysis and neural network performance for bearing fault diagnosis from vibration signal processed by RS and DF analyses. Mechanical Systems & Signal Processing 25(5): 1765–1772.

14.

Pan

Chen

Dong

(2009a) A hybrid model for bearing performance degradation assessment based on support vector data description and fuzzy c-means. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 223(11): 2687–2695.

15.

Pan

Chen

(2010b) Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means. Mechanical Systems and Signal Processing 24(2): 559–566.

16.

Shahriari

M R

(2015) A cultural algorithm for data clustering. International Journal of Industrial Mathematics 8(2): 99–106.

17.

Tong

Wang

Huang

(2005) Monitoring defects in IC fabrication using a Hotelling T2 control chart. IEEE Transactions on Semiconductor Manufacturing 18(1): 140–147.

18.

Huang

et al . (2016). Optimized relative transformation matrix using bacterial foraging algorithm for process fault detection. IEEE Transactions on Industrial Electronics 63(4): 2595–2605.

19.

(2016) Rolling Bearing Degradation State Identification Based on LCD Relative Spectral Entropy. Journal of Failure Analysis and Prevention 16(4): 655–666.

20.

Zhang

Dai

G L

(2016) Improvement of distributed clustering algorithm based on min-cluster. Optik 127(8): 3878–3881.

21.

Zhao

(2013) The study on rotating machinery early fault diagnosis based on principal component analysis and fuzzy C-means algorithm. Journal of Software 8(3): 709–715.

Life state recognition of slewing bearing with heavy load and low speed based on point density function with fuzzy C means

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