Defect detection of helical gears based on time–frequency analysis and using multi-layer fusion network

References

• Lee J, Wu F, Zhao W, et al. Prognostics and health management design for rotary machinery systems – reviews, methodology and applications. Mech Syst Signal Process. 2014;42:314–334.10.1016/j.ymssp.2013.06.004
   Web of Science ®Google Scholar
• Hasanzadeh Ghafari S. A fault diagnosis system for rotary machinery supported by rolling element bearings [PhD dissertation]. Waterloo: Department of Mechanical Engineering, University of Waterloo; 2007.
   Google Scholar
• Mba D. Condition monitoring & vibration diagnosis of rotating machines [Lecture Notes]. Cranfield: School of Engineering, Cranfield University; 2005.
   Google Scholar
• Eftekharnejad B. Condition monitoring of gearboxes using acoustic emission [Phd dissertation]. Cranfield: School of Engineering, Cranfield University; 2010.
   Google Scholar
• Ahuja N, Lertrattanapanich S, Bose NK. Properties determining choice of mother wavelet. IEE Proc-Vision Image Signal Process. 2005;152:659–664.10.1049/ip-vis:20045034
   Google Scholar
• Antonino-Daviu JA, Riera-Guasp M, Folch JR, et al. Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines. IEEE Trans Ind Appl. 2006;42:990–996.10.1109/TIA.2006.876082
   Web of Science ®Google Scholar
• Boulahbal D, Golnaraghi MF, Ismail F. Amplitude and phase wavelet maps for the detection of cracks in geared systems. Mech Syst Signal Process. 1999;13:423–436.10.1006/mssp.1998.1206
   Web of Science ®Google Scholar
• Brechet L, Lucas MF, Doncarli C, et al. Compression of biomedical signals with mother wavelet optimization and best-basis wavelet packet selection. IEEE Trans Biomed Eng. 2007;54:2186–2192.10.1109/TBME.2007.896596
   PubMed Web of Science ®Google Scholar
• Dalpiaz G, Rivola A, Rubini R. Effectiveness and sensitivity of vibration processing techniques for local fault detection in gears. Mech Syst Signal Process. 2000;14:387–412.10.1006/mssp.1999.1294
   Web of Science ®Google Scholar
• Flanders M. Choosing a wavelet for single-trial EMG. J Neurosci Methods. 2002;116:165–177.10.1016/S0165-0270(02)00038-9
   PubMed Web of Science ®Google Scholar
• Li L, Qu L, Liao X. Haar wavelet for machine fault diagnosis. Mech Syst Signal Process. 2007;21:1773–1786.10.1016/j.ymssp.2006.07.006
   Web of Science ®Google Scholar
• Lin J, Qu L. Feature extraction based on morlet wavelet and its application for mechanical fault diagnosis. J Sound Vib. 2000;234:135–148.10.1006/jsvi.2000.2864
   Web of Science ®Google Scholar
• Nikolaou NG, Antoniadis IA. Demodulation of vibration signals generated by defects in rolling element bearings using complex shifted morlet wavelets. Mech Syst Signal Process. 2002;16:677–694.10.1006/mssp.2001.1459
   Web of Science ®Google Scholar
• Rubini R, Meneghetti U. Application of the envelope and wavelet transform analyses for the diagnosis of incipient faults in ball bearings. Mech Syst Signal Process. 2001;15:287–302.10.1006/mssp.2000.1330
   Web of Science ®Google Scholar
• Singh BN, Tiwari AK. Optimal selection of wavelet basis function applied to ECG signal denoising. Digital Signal Process. 2006;16:275–287.10.1016/j.dsp.2005.12.003
   Web of Science ®Google Scholar
• Staszewski WJ, Tomlinson GR. Application of the wavelet transform to fault detection in a spur gear. Mech Syst Signal Process. 1994;8:289–307.10.1006/mssp.1994.1022
   Web of Science ®Google Scholar
• McFadden PD, Smith JD. Vibration monitoring of rolling element bearings by the high-frequency resonance technique – a review. Tribol Int. 1984;17:3–10.10.1016/0301-679X(84)90076-8
   Web of Science ®Google Scholar
• Samanta B, Al-Balushi KR. Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech Syst Signal Process. 2003;17:317–328.10.1006/mssp.2001.1462
   Web of Science ®Google Scholar
• Newland DE. Wavelet analysis of vibration: part 1—theory. J Vib Acoust. 1994;116:409–416.10.1115/1.2930443
   Web of Science ®Google Scholar
• Fan X, Zuo MJ. Gearbox fault detection using Hilbert and wavelet packet transform. Mech Syst Signal Process. 2006;20:966–982.10.1016/j.ymssp.2005.08.032
   Web of Science ®Google Scholar
• Farina D, Lucas MF, Doncarli C. Optimized wavelets for blind separation of nonstationary surface myoelectric signals. IEEE Trans Biomed Eng. 2008;55:78–86.10.1109/TBME.2007.897844
   PubMed Web of Science ®Google Scholar
• Paya BA, Esat II, Badi MNM. Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mech Syst Signal Process. 1997;11:751–765.10.1006/mssp.1997.0090
   Web of Science ®Google Scholar
• Ebrahimi E. Faults detection of gearbox of MF285 tractor based on wavelet packet transform and ANFIS. J Appl Sci Agric. 2014;9:719–727.
   Google Scholar
• Saravanan N, Cholairajan S, Ramachandran KI. Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique. Expert Syst Appl. 2009;36:3119–3135.10.1016/j.eswa.2008.01.010
   Web of Science ®Google Scholar
• Saravanan N, Siddabattuni VK, Ramachandran KI. A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box. Expert Syst Appl. 2008;35:1351–1366.10.1016/j.eswa.2007.08.026
   Web of Science ®Google Scholar
• Saravanan N, Ramachandran KI. Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Syst Appl. 2010;37:4168–4181.10.1016/j.eswa.2009.11.006
   Web of Science ®Google Scholar
• Rafiee J, Tse PW, Harifi A, et al. A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system. Expert Syst Appl. 2009;36:4862–4875.10.1016/j.eswa.2008.05.052
   Web of Science ®Google Scholar
• Rafiee J, Rafiee MA, Tse PW. Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Syst Appl. 2010;37:4568–4579.10.1016/j.eswa.2009.12.051
   Web of Science ®Google Scholar
• Rafiee J, Arvani F, Harifi A, et al. Intelligent condition monitoring of a gearbox using artificial neural network. Mech Syst Signal Process. 2007;21:1746–1754.10.1016/j.ymssp.2006.08.005
   Web of Science ®Google Scholar
• Rafiee J, Tse PW. Use of autocorrelation of wavelet coefficients for fault diagnosis. Mech Syst Signal Process. 2009;23:1554–1572.10.1016/j.ymssp.2009.02.008
   Web of Science ®Google Scholar
• Samanta B. Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mech Syst Signal Process. 2004;18:625–644.10.1016/S0888-3270(03)00020-7
   Web of Science ®Google Scholar
• Baydar N, Ball A. A comparative study of acoustic and vibration signals in detection of gear failures using Wigner–Ville distribution. Mech Syst Signal Process. 2001;15:1091–1107.10.1006/mssp.2000.1338
   Web of Science ®Google Scholar
• Peng ZK, Chu FL. Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process. 2004;18:199–221.10.1016/S0888-3270(03)00075-X
   Web of Science ®Google Scholar
• Li Z, Ma Z, Liu Y, et al. Crack fault detection for a gearbox using discrete wavelet transform and an adaptive resonance theory neural network. Strojniški vestnik-J Mech Eng. 2015;61:63–73.10.5545/sv-jme
   Web of Science ®Google Scholar
• Khajavi MN, Keshtan MN. 1190. Intelligent fault classification of rolling bearings using neural network and discrete wavelet transform. J Vibroeng. 2014;16:761–769.
   Google Scholar
• Li XF, Fu YX, Jia LM. Fault diagnosis of railway axlebox bearing based on wavelet packet and neural network. In: Applied Mechanics and Materials. Vol. 226, 2012 International Conference on Vibration, Structural Engineering and Measurement, ICVSEM 2012; Shanghai; Trans Tech Publications; 2012. p. 749–755.
   Google Scholar
• Jardine AK, Lin D, Banjevic D. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process. 2006;20:1483–1510.10.1016/j.ymssp.2005.09.012
   Web of Science ®Google Scholar
• Daubechies I. Ten lectures on wavelets. Vol. 61. Philadelphia: Society for industrial and applied mathematics; 1992. p. 198–202.
   Google Scholar
• Coifman RR, Meyer Y, Wickerhauser MV. Wavelet analysis and signal processing. In: MB Ruskai, G Beylkin, RR Coifman, I Daubechies, S Mallat, Y Meyer, L Raphael, editors. Wavelets and their applications. Boston (MA): Jones and Bartlett; 1991. p. 153–178.
   Google Scholar
• Coifman RR, Meyer Y, Wickerhauser MV. Wavelet analysis and signal processing. In: MB Ruskai, G Beylkin, RR Coifman, I Daubechies, S Mallat, Y Meyer, L Raphael, editors. Wavelets and their applications. Boston (MA): Jones and Bartlett; 1991. p. 453–470.
   Google Scholar
• Avci E. A new optimum feature extraction and classification method for speaker recognition: GWPNN. Expert Syst Appl. 2007;32:485–498.10.1016/j.eswa.2005.12.004
   Web of Science ®Google Scholar
• Avci E, Akpolat ZH. Speech recognition using a wavelet packet adaptive network based fuzzy inference system. Expert Syst Appl. 2006;31:495–503.10.1016/j.eswa.2005.09.058
   Web of Science ®Google Scholar
• Avci E, Turkoglu I, Poyraz M. Intelligent target recognition based on wavelet packet neural network. Expert Syst Appl. 2005;29:175–182.10.1016/j.eswa.2005.01.016
   Web of Science ®Google Scholar
• Ghaffari A, Ebrahimi Orimi H. EEG signals classification of epileptic patients via feature selection and voting criteria in intelligent method. J Med Eng Technol. 2014;38:146–155.
   PubMedGoogle Scholar