Diagnosis Method of Lubrication Failure by Coolant Immersion for a CNC Lathe Spindle
Keigo Takasugi*, , Naohiko Suzuki**, Yoshiyuki Kaneko**, and Naoki Asakawa*
*Institute of Science and Engineering, Kanazawa University
Kakuma-machi, Kanazawa-shi, Ishikawa 920-1192, Japan
**Takamatsu Machinery Co., Ltd.
As a result of the development of network technologies, diagnosis techniques that can collect machine states continuously and prognostic health management (PHM) are available in the factory. PHM technology is also beginning to be implemented in the machine tool field. However, few studies have described causality between feature values, including vibration and acoustic emission data, collected by machine and physical phenomena of failures under the actual use of machine tools. In the present paper, a PHM system of lubrication failure of bearings in CNC lathe spindles is developed. An acceleration sensor is used to collect machine states, and statistical feature parameters that characterize the lubrication failure are extracted from the obtained vibration data. Moreover, in order to clarify the cause-effect relation between the extracted feature parameters and physical phenomena of lubrication failure, several analyses using surface roughness measurement, residual stress measurement, and grease consistency measurement are conducted.
-  J. Lee, F. Wu, W. Zhao, M. Ghaffari, L. Liao, and D. Siegel, “Prognostics and health management design for rotary machinery systems – reviews, methodology and applications,” Mechanical Systems and Signal Processing, Vol.42, Nos.1-2, pp. 314-334, 2014.
-  R. Ahmad and S. Kamaruddin, “An overview of time-based and condition-based maintenance in industrial application,” Computer & Industrial Eng., Vol.63, No.1, pp. 135-149, 2012.
-  D. Goyal and B. S. Pabla, “Condition based maintenance of machine tools – A review,” CIRP J. of Manufacturing Science and Technology, Vol.10, pp. 24-35, 2015.
-  G. E. P. Box, G. M. Jenkins, G. C. Reinsel, and G. M. Ljung, “Time Series Analysis, Forecasting and Control,” Prentice Hall, 1994.
-  P. D. McFadden and J. D. Smith, “Vibration monitoring of rolling element bearings by the high-frequency resonance technique – a review,” Tribology Int., Vol.17, No.1, pp. 3-10, 1984.
-  A. Glowacz, W. Glowacz, Z. Glowacz, and J. Kozik, “Early fault diagnosis of bearing and stator faults of the single-phase induction motor using acoustic signals,” Measurement, Vol.113, pp. 1-9, 2018.
-  H. Yang, J. Mathew, and L. Ma, “Fault diagnosis of rolling element bearings using basis pursuit,” Mechanical Systems and Signal Processing, Vol.19, No.2, pp. 341-356, 2005.
-  R. Yan, R. X. Gao, and X. Chen, “Wavelets for fault diagnosis of rotary machines: A review with applications,” Signal Processing, Vol.96, Part A, pp. 1-15, 2014.
-  Z. K. Peng, P. W. Tse, and F. L. Chu, “A comparison study of improved Hilbert–Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing,” Mechanical Systems and Signal Processing, Vol.19, No.5, pp. 974-988, 2005.
-  V. K. Rai and A. R. Mohanty, “Bearing fault diagnosis using FFT of intrinsic mode functions in Hilbert–Huang transform,” Mechanical Systems and Signal Processing, Vol.21, No.6, pp. 2607-2615, 2007.
-  T. Williams, X. Ribadeneira, S. Billington, and T. Kurfess, “Rolling Element Bearing Diagnostics in Run-to-Failure Lifetime Testing,” Mechanical Systems and Signal Processing, Vol.15, No.5, pp. 979-993, 2001.
-  S. F. Zhao, L. Liang, G. H. Xu, J. Wang, and W. M. Zhang, “Quantitative diagnosis of a spall-like fault of a rolling element bearing by empirical mode decomposition and the approximate entropy method,” Mechanical Systems and Signal Processing, Vol.40, No.1, pp. 154-177, 2013.
-  X. Zhang, Y. Liang, J. Zhou, and Y. Zang, “A novel bearing fault diagnosis model integrated permutation entropy, ensemble empirical mode decomposition and optimized SVM,” Measurement, Vol.69, pp. 164-179, 2015.
-  K. Kappaganthu and C. Nataraj, “Nonlinear modeling and analysis of a rolling element bearing with a clearance,” Commun. Nonlinear Sci. Numer. Simulat., Vol.16, No.10, pp. 4134-4145, 2011.
-  L. Niu, H. Cao, Z. He, and Y. Li, “A systematic study of ball passing frequencies based on dynamic modeling of rolling ball bearings with localized surface defects,” J. of Sound and Vibration, Vol.357, No.24, pp. 207-232, 2015.
-  J. Liu and Y. Shao, “Dynamic modeling for rigid rotor bearing systems with a localized defect considering additional deformations at the sharp edges,” J. of Sound and Vibration, Vol.398, No.23, pp. 84-102, 2017.
-  J. Liu, C. Tang, and Y. Shao, “An innovative dynamic model for vibration analysis of a flexible roller bearing,” Mechanism and Machine Theory, Vol.135, pp. 27-39, 2019.
-  A. M. Ahmadi, D. Petersen, and C. Howard, “A nonlinear dynamic vibration model of defective bearings – The importance of modelling the finite size of rolling elements –,” Mechanical Systems and Signal Processing, Vols.52-53, pp. 309-326, 2015.
-  F. Jia, Y. Lei, J. Lin, X. Zhou, and N. Lu, “Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data,” Mechanical Systems and Signal Processing, Vols.72-73, pp. 303-315, 2016.
-  X. Guo, L. Chen, and C. Shen, “Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis,” Measurement, Vol.93, pp. 490-502, 2016.
-  D. Choi, W. T. Kwon, and C. N. Chu, “Real-time monitoring of tool fracture in turning using sensor fusion,” Int. J. of Advanced Manufacturing Technology, Vol.15, No.5, pp. 305-310, 1999.
-  S. N. Huang, K. K. Tan, Y. S. Wong, C. W. de Silva, H. L. Goh, and W. W. Tan, “Tool wear detection and fault diagnosis based on cutting force monitoring,” Int. J. of Machine Tools and Manufacture, Vol.47, Nos.3-4, pp. 444-451, 2007.
-  T. Boutros and M. Liang, “Detection and diagnosis of bearing and cutting tool faults using hidden Markov models,” Mechanical Systems and Signal Processing, Vol.25, No.6, pp. 2102-2124, 2011.
-  P. J. Withers and H. K. D. H. Bhadeshia, “Residual stress Part 1 – Measurement techniques,” Materials Science and Technology, Vol.17, No.4, pp. 355-365, 2001.
-  P. J. Withers and H. K. D. H. Bhadeshia, “Residual stress Part 2 – Nature and origins,” Materials Science and Technology, Vol.17, No.4, pp. 366-375, 2001.
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