RESEARCH PAPER
Gearbox faults feature selection and severity classification using machine learning
1
Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovica 6, 21000 Novi Sad, Serbia
2
E.ON UK, Westwood Way, Westwood Business Park, Coventry CV4 8LG, United Kingdom
Publication date: 2020-12-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2020;22(4):748-756
HIGHLIGHTS
- Gearbox health evaluation using vibration features from both time and frequency domains
- Demonstration of importance of data dimension reduction for reliable conclusions.
- Indicators from cepstrum functions had the highest influence in modeling data set.
- Classic impulsiveness indicators had a minor influence in modeling data set.
KEYWORDS
ABSTRACT
The most widely used technique for gearbox fault diagnosis is still vibration analysis. The
need for gearbox condition monitoring in an automated process is essential and there is still
a problem with the selection of features that best describe a fault or its severity level. For this
purpose, multiple-domain vibration signals statistic features are extracted through time and
frequency domain by postprocessing of raw time signal, time-synchronous average signal,
frequency spectra and cepstrum. Five different datasets are considered with different levels
of fault analyzing gear chipped and a missing tooth, gear root crack, and gear tooth wear
under stable running speed and load. A preliminary experimental study of a single stage test
bench gearbox was performed in order to test feature sensitivity to type and level of fault in
the process of clustering and classification. Selected features were finally processed using
an artificial neural network classifier.
REFERENCES (32)
1.
Antoni J, Borghesani P. A statistical methodology for the design of condition indicators. Mechanical Systems and Signal Processing 2019; 114: 290-237, https://doi.org/10.1016/j.ymss....
2.
Baillie D, Mathew J. A comparison of autoregressive modeling techniques for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing 1996; 10: 1-17, https://doi.org/10.1006/mssp.1....
3.
Bajrić R. Contribution to the identification of gear pairs damage by using mechanical vibration signal analysis techniques. PhD Thesis, Faculty of Technical Sciences -University of Novi Sad 2016.
4.
Chen Y, Liang X, Zuo MJ. Sparse time series modeling of the baseline vibration from a gearbox under time-varying speed condition. Mechanical Systems and Signal Processing 2019; 134: 106342, https://doi.org/10.1016/j.ymss....
5.
Dhamande LS, Chaudhari MB. Compound gear-bearing fault feature extraction using statistical features based on time-frequency method.Measurement 2018; 125: 63-77, https://doi.org/10.1016/j.meas....
6.
Gan M, Wang C, Zhu C. Multiple-domain manifold for feature extraction in machinery fault diagnosis Measurement 2015; 75: 76-91, https://doi.org/10.1016/j.meas....
7.
He Q, Yan R, Fanrang K, Du R. Machine condition monitoring using principal component representations. Mechanical Systems and Signal Processing 2009; 23(2): 446-466, https://doi.org/10.1016/j.ymss....
8.
Ibrahim G, Albarbar A, Abouhnik A, Shnibha R. Adaptive filtering based system for extracting gearbox condition feature from the measured vibrations. Measurement 2013; 46 (6): 2029-2034, https://doi.org/10.1016/j.meas....
9.
Jardine AK, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing 2006; 20(7): 1483-1510, https://doi.org/10.1016/j.ymss....
10.
Jia F, Lei Y, Lin J, Zhou X, Lu N. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing 2016, 72: 303-315, https://doi.org/10.1016/j.ymss....
11.
Karabacak YE, Gursel Özmen N, Gumusel L. Worm gear condition monitoring and fault detection from thermal images via deep learning method. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22 (3): 544-556, https://doi.org/10.17531/ein.2....
13.
Lazarz B, Wojnar G, Czech P. Early fault detection of toothed gear in exploitation conditions. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2011; 1(49): 68-77.
14.
Li X, Li J, He D, Qu Y. Gear pitting fault diagnosis using raw acoustic emission signal based on deep learning. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2019; 21 (3): 403-410, https://doi.org/10.17531/ein.2....
15.
Li Y, Wang K. Modified convolutional neural network with global average pooling for intelligent fault diagnosis of industrial gearbox. Eksploatacja i niezawodnosc - Maintenance and Reliability 2020; 22 (1): 63-72, https://doi.org/10.17531/ein.2....
16.
Liu S, Hou S, He K, Yang W. L-kurtosis and its application for fault detection of rolling element bearings, Measurement 2018, 116: 523-532, https://doi.org/10.1016/j.meas....
17.
Loutas TH, Roulias D, Pauly E, Kostopoulos V. The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery. Mechanical Systems and Signal Processing 2011; 25 (4): 1339-1352, https://doi.org/10.1016/j.ymss....
18.
Lu D, Qiao W, Gong X. Current-based gear fault detection for wind turbine gearboxes, IEEE Transactions on Sustainable Energy 2017, 8 (4): 1453-1462, https://doi.org/10.1109/TSTE.2....
19.
Malhi A, Gao R X. PCA-based feature selection scheme for machine defect classification. IEEE Transactions on Instrumentation and Measurement 2004, 53 (6): 1517 - 1525, https://doi.org/10.1109/TIM.20....
20.
Omar FK, Gaouda A. Dynamic wavelet-based tool for gearbox diagnosis. Mechanical Systems and Signal Processing 2012; 26(1): 190-204, https://doi.org/10.1016/j.ymss....
21.
Rafiee J, Rafiee M, Tse P. Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Systems with Applications 2010, 37: 4568-4579, https://doi.org/10.1016/j.eswa....
22.
Schweizer K, Cattin PC, Brunner R, Müller B, Huber C, Romkes J. Automatic selection of a representative trial from multiple measurements using Principle Component Analysis, Journal of biomechanics 2012; 45: 2306-2309, https://doi.org/10.1016/j.jbio....
23.
Stetco A, Dinmohammadi F, Zhao X, Robu V, Flynn D, Barnes Keane MJ, Nenadic G. Machine learning methods for wind turbine condition monitoring: A review. Renewable Energy 2019; 133: 620-635, https://doi.org/10.1016/j.rene....
24.
Wang D. K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited. Mechanical Systems and Signal Processing 2016; 70-71: 201-208, https://doi.org/10.1016/j.ymss....
25.
Wang L, Shao Y. Fault feature extraction of rotating machinery using a reweighted complete ensemble empirical mode decomposition with adaptive noise and demodulation analysis. Mechanical Systems and Signal Processing 2020; 138: 106545, https://doi.org/10.1016/j.ymss....
26.
Wang W.Q., Ismail F., Golnaraghi M. Assessment of gear damage monitoring techniques using vibration measurements. Mechanical Systems and Signal Processing 2001; 15 (5): 905-922, https://doi.org/10.1006/mssp.2....
27.
YanPing Z, ShuHong H, JingHong H, Tao S, Wei L. Continuous wavelet grey moment approach for vibration analysis of rotating machinery, Mechanical Systems and Signal Processing 2006; 20(5): 1202-1220, https://doi.org/10.1016/j.ymss....
28.
Widodo A, Yang BS, Han T. Combination of independent component analysis and support vector machines for intelligent faults diagnosis of induction motors. Expert Systems with Applications 2007; 32(2): 299-312, https://doi.org/10.1016/j.eswa....
29.
Ziaran S, Darula R. Determination of the state of wear of high contact ratio gear sets by means of spectrum and cepstrum analysis. Journal of Vibration and Acoustics 2013; 135(2): 021008, https://doi.org/10.1115/1.4023....
30.
Zhang X, Zhao J. Compound fault detection in gearbox based on time synchronous resample and adaptive variational mode decomposition. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2020; 22 (1): 161-169, https://doi.org/10.17531/ein.2....
31.
ISO 21771: 2007 Gears - Cylindrical involute gears and gear pairs - Concepts and geometry.
32.
SOM Toolbox for Matlab, laboratory for information and computer science, University of Helsinki, http://www.cis.hut.fi/projects....
CITATIONS (10):
1.
Dynamic Analysis Method for Fault Propagation Behaviour of Machining Centres
Liming Mu, Yingzhi Zhang, Jintong Liu, Fenli Zhai, Jie Song
Applied Sciences
Liming Mu, Yingzhi Zhang, Jintong Liu, Fenli Zhai, Jie Song
Applied Sciences
2.
Alarms management by supervisory control and data acquisition system for wind turbines
Ramirez Segovia, Behnam Mohammadi-Ivatloo, Márquez García
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Ramirez Segovia, Behnam Mohammadi-Ivatloo, Márquez García
Eksploatacja i Niezawodnosc - Maintenance and Reliability
3.
Application of Predictive Maintenance Concepts Using Artificial Intelligence Tools
Diogo Cardoso, Luís Ferreira
Applied Sciences
Diogo Cardoso, Luís Ferreira
Applied Sciences
4.
Framework of machine criticality assessment with criteria interactions
Małgorzata Jasiulewicz–Kaczmarek, Katarzyna Antosz, Patryk Żywica, Dariusz Mazurkiewicz, Bo Sun, Yi Ren
Eksploatacja i Niezawodność – Maintenance and Reliability
Małgorzata Jasiulewicz–Kaczmarek, Katarzyna Antosz, Patryk Żywica, Dariusz Mazurkiewicz, Bo Sun, Yi Ren
Eksploatacja i Niezawodność – Maintenance and Reliability
5.
Development of a Prediction Model for the Gear Whine Noise of Transmission Using Machine Learning
Sun-Hyoung Lee, Kwang-Phil Park
International Journal of Precision Engineering and Manufacturing
Sun-Hyoung Lee, Kwang-Phil Park
International Journal of Precision Engineering and Manufacturing
6.
Block feature selection based on NSGA-II applied to fault diagnosis of gearboxes
Xianhua Chen, Zhigang Tian, Meng Rao
Advanced Engineering Informatics
Xianhua Chen, Zhigang Tian, Meng Rao
Advanced Engineering Informatics
7.
Proceedings of the 11th IFToMM International Conference on Rotordynamics
Pantha Das, Rajiv Tiwari, Dhruba Bordoloi
Pantha Das, Rajiv Tiwari, Dhruba Bordoloi
8.
Machine learning scopes on microgrid predictive maintenance: Potential frameworks, challenges, and prospects
M.Y. Arafat, M.J. Hossain, Md Alam
Renewable and Sustainable Energy Reviews
M.Y. Arafat, M.J. Hossain, Md Alam
Renewable and Sustainable Energy Reviews
9.
Sound Based Degradation Status Recognition for Railway Point Machines Based on Soft-Threshold Wavelet Denoising, WPD, and ReliefF
Yongkui Sun, Yuan Cao, Peng Li, Shuai Su
IEEE Transactions on Instrumentation and Measurement
Yongkui Sun, Yuan Cao, Peng Li, Shuai Su
IEEE Transactions on Instrumentation and Measurement
10.
Selection of the level of vibration signal decomposition and mother wavelets to determine the level of failure severity in spur gearboxes
Antonio Pérez‐Torres, Rene‐Vinicio Sánchez, Susana Barceló‐Cerdá
Quality and Reliability Engineering International
Antonio Pérez‐Torres, Rene‐Vinicio Sánchez, Susana Barceló‐Cerdá
Quality and Reliability Engineering International
Share
RELATED ARTICLE
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
We process personal data collected when visiting the website. The function of obtaining information about users and their behavior is carried out by voluntarily entered information in forms and saving cookies in end devices. Data, including cookies, are used to provide services, improve the user experience and to analyze the traffic in accordance with the Privacy policy. Data are also collected and processed by Google Analytics tool (more).
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
You can change cookies settings in your browser. Restricted use of cookies in the browser configuration may affect some functionalities of the website.
