Fault analysis on continuous variable transmission using DB-06 wavelet decomposition and fault classification using ANN

Abstract

This study aims at developing a novel method for condition monitoring technique for detection and classification of developing faults and increase the working life of continuous variable transmission (CVT) using Daubechies Wavelet 06 (DB-06). The vibration data is collected for 4 different types of faults and healthy condition. Using a magnetic accelerometer and signal analyser, vibration data is collected from the system in the time-domain which is then used as input for a MATLAB code producing the plot of the frequency-domain signal. Maximum frequency is determined to diagnose the faults which are induced over three different belts. Collected data for large scale automotive system (CVT) is used to train the network and then it is tested based on random data points. Faults were classified using ANN with a classification rate of 90.8 %.

Keywords

Continuous variable transmission (CVT)Daubechies wavelet fault diagnosis fault classification artificial neural network

Get full access to this article

View all access options for this article.

References

Narendiranath Babu

, Manvel Raj

and Lakshmanan

, A Review on Application of Dynamic Parameters of Journal Bearing for Vibration and Condition Monitoring, Journal of Mechanics 31(4) (2015), 391–416. https://doi.org/10.1017/jfm.2015.6

Lebrecht

, Pfeiffer

and Heinz

, Analysis of self-induced vibrations in a pushing V-belt CVT, SAE Technical Paper 40 (2019), 32. https://doi.org/10.4271/2012-01-0309

Rafiee

, Rafiee

M.A.

and Tse

P.W.

, Application of mother wavelet functions for automatic gear and bearing fault diagnosis, Expert Systems with Applications, 4568–4579, ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2009.12.051

Nizwan

C.K.

, Ong

S.A.

, Yusof

M.F.

and Baharom

M.Z.

, A wavelet decomposition analysis of vibration signal for bearing fault detection, InIOP Conference Series: Materials Science and Engineering, (2013), 50. https://doi.org/10.1088/1757-899X/50/1/012026

Babu,

Narendiranath

, Himanshu

H.S.

, Kumar

, Rama

and Nishant

, Journal Bearing Fault Detection Based on Daubechies Wavelet, Archives of Acoustics, (2017), 42. https://doi.org/10.1515/aoa-2017-0042

Hariharan

and Pss

Srinivasan

, New approach of classification of rolling element bearing fault using artificial neural network, Journal of Mechanical Engineering, (2010). https://doi.org/10.3329/jme.v40i2.5353

Del Grande

and Testi

, FEM Analysis of a CVT Pulley, Materials Testing, (2008), 245–249. https://doi.org/10.3139/120.100814

Shi

, Kouri

D.J.

, Wei

G.W.

and Hoffman

D.K.

, Generalized symmetric interpolating wavelets, Computer Physics Communications 119(2–3) (1999), 194–218, ISSN 0010-4655. https://doi.org/10.1016/S0010-655(99)00185-X

Bastami

A.R.

and Vahid

, Estimating the size of naturally generated defects in the outer ring and roller of a tapered roller bearing based on autoregressive model combined with envelope analysis and discrete wavelet transform, Measurement 159 (2020), 107767, ISSN 0263-2241. https://doi.org/10.1016/j.measurement.2020.107767

10.

Tangjitsitcharoen

and Lohasiriwat

, Hybrid Monitoring of Chip Formation and Straightness in CNC Turning by Utilizing Daubechies Wavelet Transform, Procedia Manufacturing 25 (2018), 279–286, ISSN 2351-9789. https://doi.org/10.1016/j.promfg.2018.06.084

11.

Zhao

, Yang

, Li

, Liu

, Gu

and Liu

, A new bearing fault diagnosis method based on signal-to-image mapping and convolutional neural network, Measurement 176 (2021), 109088, ISSN 02632241. https://doi.org/10.1016/j.measurement.2021.109088

12.

Wang

, Xu

, Yan

, Sun

and Chen

, Intelligent Bearing Fault Diagnosis Using Multi-Head Attention-Based CNN, Procedia Manufacturing 49 (2020), 112–118, ISSN 2351-9789. https://doi.org/10.1016/j.promfg.2020.07.005

13.

, Shao

, Zhong

and Zhao

, Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions, Knowledge-Based Systems 207 (2020), 106396, ISSN 950-7051. https://doi.org/10.1016/j.knosys.2020.106396

14.

Zhao

, Zhang

, Li

and Yang

, Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions, Knowledge-Based Systems 199 (2020), 105971, ISSN 0950-7051. https://doi.org/10.1016/j.knosys.2020.105971

15.

Dibaj

, Ettefagh

M.M.

, Hassannejad

and Ehghaghi

M.B.

, A hybrid fine-tuned VMD and CNN scheme for untrained compound fault diagnosis of rotating machinery with unequal-severity faults, Expert Systems with Applications 167 (2021), 114094, ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2020.114094

16.

Zhong

S.-S.

, Fu

and Haigh

, A novel gas turbine fault diagnosis method based on transfer learning with CNN, Measurement 137 (2241), 435–453, ISSN 0263-2241. https://doi.org/10.1016/j.measurement.2019.01.022