Gearbox fault diagnosis is a crucial aspect of maintenance and reliability in automobile engineering. In automobile vehicles, the gearbox is a vital component that facilitates efficient power transfer from the engine to the wheels, enabling optimal performance, speed control, and fuel efficiency. Therefore, early diagnosis of gearbox faults is crucial to avoid severe damage to the gearbox and other parts. This study aims to develop a novel hybrid deep learning method for gearbox fault detection and classification. It acquires statistical characteristics, higher-order statistical features, modified log-energy entropy, modified Renyi entropy, and the Shannon feature. A bidirectional long-short-term memory (Bi-LSTM) and a recurrent neural network (RNN) detect faults. Opposition learning combined with an artificial humming-based crow search algorithm (OAHCSA) determines the RNN weights. The outputs are further averaged to obtain more accurate findings. The proposed OAHCSA with the HC model achieves 99.62% accuracy at 15 Hz, 98.85% at 20 Hz, 99.23% at 25 Hz, and 99.23% at 30 Hz. The findings show that the proposed method has the potential to be an effective alternative for precise gear fault detection.
AhujaASRamtekeDSPareyA.Vibration-based fault diagnosis of a bevel and spur gearbox using continuous wavelet transform and adaptive neuro-fuzzy inference system. In: MalikHIqbalAYadavA (eds) Soft computing in condition monitoring and diagnostics of electrical and mechanical systems: novel methods for condition monitoring and diagnostics. Singapore: Springer, 2020, pp.473–496.
2.
BabuTNAliPSPrabhaDR, et al. Fault diagnosis in bevel gearbox using coiflet wavelet and fault classification based on ANN including DNN. Arab J Sci Eng2022; 47(12): 15823–15849.
3.
Contreras UrgilésRWOrtegaJMRocanoE, et al. Classification of mechanical failures in provoked ignition engine by means of ANN and SVM. In: International conference on science, technology and innovation for society, Guayaquil, Ecuador, 22–24 June 2022.
4.
KimHSMinJKSongJB.Multiple-degree-of-freedom counterbalance robot arm based on slider-crank mechanism and bevel gear units. IEEE Trans Robot2016; 32: 230–235.
5.
HajnayebAFernandoJSSunQ.Effects of vehicle driveline parameters and clutch judder on gearbox vibrations. Proc IMechE, Part D: J Automobile Engineering2022; 236(1): 84–98.
6.
SannSTomehEPetrT.Methods of detection and localization of the sources of noise and vibration on car gearboxes: a review. J Vibroengineering2024; 26: 808–842.
7.
ZhaoDLiJChengW, et al. Generalized demodulation transform for bearing fault diagnosis under nonstationary conditions and gear noise interferences. Chin J Mech Eng (English Ed)2019; 32: 1–11.
8.
ZhouZLiTZhaoZ, et al. Differentiable architecture search for aeroengine bevel gear fault diagnosis. In: International conference on sensing, measurement and data analytics in the era of artificial intelligence, ICSMD 2020 - proceedings, Xi’an, China, 15–17 October 2020, pp.270–274. New York: IEEE.
9.
MuYHeX.Design and dynamic performance analysis of high-contact-ratio spiral bevel gear based on the higher-order tooth surface modification. Mech Mach Theory2021; 161: 104312.
10.
KimJHanHTKangS, et al. Development of novel bevel-geared 5 mm articulating wrist for micro-laparoscopy instrument. IEEE Robot Autom Lett2019; 4(4): 3711–3718.
11.
MuYHeXFangZ.An innovative ease-off flank modification method based on the dynamic performance for high-speed spiral bevel gear with high-contact-ratio. Mech Mach Theory2021; 162: 104345.
12.
ZhaoWWangLMirjaliliS.Artificial hummingbird algorithm: a new bio-inspired optimizer with its engineering applications. Comput Methods Appl Mech Eng2022; 388: 114194.
13.
SunLZhouYHuangH, et al. Analysis and design of a spatial planetary noncircular gear train for rice seedling transplanting based on three given positions. Trans ASABE2020; 63(1): 165–176.
14.
LiYZhouZSunC, et al. Domain adaptive sparse transformer for aeroengine bevel gear fault diagnosis. In: ICSMD 2021 - 2nd international conference on sensing, measurement and data analytics in the era of artificial intelligence, Nanjing, China, 21–23 October 2021, pp.1–6. New York: IEEE.
15.
ShangZZhaoZZhouZ, et al. Denoising fused wavelets net for aeroengine bevel gear fault diagnosis. In: ICSMD 2021 - 2nd international conference on sensing, measurement and data analytics in the era of artificial intelligence, Nanjing, China, 21–23 October 2021, pp.1–6. New York: IEEE.
16.
WangK. Phase information at tooth mesh frequency for gear crack diagnosis. In: 2007 2nd IEEE conference on industrial electronics and applications, Harbin, China, 23–25 May 2007, pp.2712–2717. New York: IEEE.
17.
MushtaqSIslamMMSohaibM.Deep learning aided data-driven fault diagnosis of rotatory machine: a comprehensive review. Energies2021; 14(16): 5150.
18.
SaravananNKumar SiddabattuniVNSRamachandranKI. Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM). Appl Soft Comput2010; 10(1): 344–360.
19.
SaravananNKumar SiddabattuniVNSRamachandranKI. 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 Appl2008; 35(3): 1351–1366.
20.
SaravananNRamachandranKI. Fault diagnosis of spur bevel gear box using discrete wavelet features and Decision Tree classification. Expert Syst Appl2009; 36(5): 9564–9573.
21.
SaravananNCholairajanSRamachandranKI.Vibration-based fault diagnosis of spur bevel gear box using fuzzy technique. Expert Syst Appl2009; 36: 3119–3135.
22.
DiZYShaoHDXiangJW.Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions. Sci China Technol Sci2021; 64: 481–492.
23.
KeleşoğluCKüçükHDemetgülM.Fault diagnosis of bevel gears using neural pattern recognition and MLP neural network algorithms. Int J Precis Eng Manuf2020; 21: 843–856.
24.
RamtekeDSPachoriRBPareyA.Automated gearbox fault diagnosis using entropy-based features in flexible analytic wavelet transform (FAWT) domain. J Vib Eng Technol2021; 9: 1703–1713.
25.
YaoYZhangSYangS, et al. Learning attention representation with a multi-scale CNN for gear fault diagnosis under different working conditions. Sensors2020; 20(4): 1233.
26.
MonteiroRPCerradaMCabreraDR, et al. Using a support vector machine based decision stage to improve the fault diagnosis on gearboxes. Comput Intell Neurosci2019; 2019(1): 1383752.
27.
BettaharTRahmouneCBenazzouzD, et al. New method for gear fault diagnosis using empirical wavelet transform, Hilbert transform, and cosine similarity metric. Adv Mech Eng2020; 12(6): 1687814020927208.
28.
ChengJYangYWuZ, et al. Ramanujan Fourier mode decomposition and its application in gear fault diagnosis. IEEE Trans Industr Inform2021; 18(9): 6079–6088.
29.
PengCYParkYJ.A new hybrid under-sampling approach to imbalanced classification problems. Appl Artif Intell2022; 36(1): 1975393.
30.
IkhlefBRahmouneCToufikB, et al. Gearboxes fault detection under operation varying condition based on MODWPT, Ant colony optimization algorithm and Random Forest classifier. Adv Mech Eng2021; 13(8): 16878140211043004.
31.
ZhuYHuangC.An improved median filtering algorithm for image noise reduction. Phys Procedia2012; 25: 609–616.
32.
GuptaVPriyaTYadavAK, et al. Automated detection of focal EEG signals using features extracted from flexible analytic wavelet transform. Pattern Recognit Lett2017; 94: 180–188.
33.
GünerSCebeciHİAntunesJJM, et al. Sustainable efficiency drivers in Eurasian airports: fuzzy NDEA approach based on Shannon’s entropy. J Air Transp Manag2021; 92: 102039.
34.
AydınSSaraoǧluHMKaraS.Log energy entropy-based EEG classification with multilayer neural networks in seizure. Ann Biomed Eng2009; 37: 2626–2630.
35.
SharmaVPareyA.A review of gear fault diagnosis using various condition indicators. Procedia Eng2016; 144: 253–263.
36.
PareyAEl BadaouiMGuilletF, et al. Dynamic modelling of spur gear pair and application of empirical mode decomposition-based statistical analysis for early detection of localized tooth defect. J Sound Vib2006; 294: 547–561.
37.
KaoLJChiuCC.Application of integrated recurrent neural network with multivariate adaptive regression splines on SPC-EPC process. J Manuf Syst2020; 57: 109–118.
38.
EsmaelBArnaoutAFruhwirthR, et al. A statistical feature-based approach for operations recognition in drilling time series. Int J Comput Inf Syst Ind Manag Appl2012; 4(6): 100–108.
39.
KumarAPareyAKankarPK.A new hybrid LSTM-GRU model for fault diagnosis of polymer gears using vibration signals. J Vib Eng Technol2024; 12(2): 2729–2741.
40.
ZhouXLinJZhangZ, et al. Improved itracker combined with bidirectional long short-term memory for 3D gaze estimation using appearance cues. Neurocomputing2020; 390: 217–225.
BenoMMValarmathiIRSwamyMS, et al. Threshold prediction for segmenting tumour from brain MRI scans. Int J Imaging Syst Technol2014; 24: 129–137.
43.
ThomasRRangacharM.Hybrid optimization based DBN for face recognition using low-resolution images. Multimed Res2018; 1: 33–43.
44.
ShareefSMRaoRS.A hybrid learning algorithm for optimal reactive power dispatch under unbalanced conditions. J Comput Mech Power Syst Control2018; 1(1): 26–33.
45.
DevagnanamJElangoNM.Optimal resource allocation of cluster using hybrid grey wolf and cuckoo search algorithm in cloud computing. J Netw Commun Syst2020; 3(1): 31–40.
46.
PolatK.The fault diagnosis based on deep long short-term memory model from the vibration signals in the computer numerical control machines. J Inst Electron Comput2020; 2: 72–92.
47.
ChenZQLiCSanchezRV.Gearbox fault identification and classification with convolutional neural networks. Shock Vib2015; 1: 390134.
