Clutches are prone to failure owing to extended heat exposure and high levels of abrasion during power transfer. Internal damage, downtime, and permanent transmission system lock-up all can result from these faults. To detect and diagnose these faults, this study employs the deep learning approach. Vibration signals were obtained from a test rig that was exposed to various clutch conditions at various loads. The amount of data points (signal length) when collecting vibration signals from a test rig can have a significant effect on the accuracy of results. A shorter sample length can lead to an increased uncertainty in the results, while a longer sample length can lead to more accurate results. A longer sample length also increases the computational complexity of the diagnosis process, which can lead to longer execution times. In this study vibration signals were collected for various sample lengths to find the optimal sample length for systemic clutch fault diagnostics. The collected vibration signals are analyzed and transformed into vibration plots that serve as input to the deep learning pretrained network. VGG-16 model was considered for this study to diagnose the clutch system faults. Based on the outcomes, the optimal sample length for the no load condition was identified as 4000, while for the 5-kg load and 10-kg load conditions 3000 sample length was suggested for fault diagnosis of the clutch system.
SinghPJoshiSDPatneyRK, et al. Fourier-Based Feature extraction for classification of EEG signals using EEG rhythms. Circuits Syst Signal Process2016; 35: 3700–3715. Epub ahead of print 2016.
2.
ZhangKMaCXuY, et al. Feature extraction method based on adaptive and concise empirical wavelet transform and its applications in bearing fault diagnosis. Measurement (Lond)2021; 172, 108976. Epub ahead of print 2021.
3.
KavathekarSUpadhyayNKankarPK. Fault classification of ball bearing by rotation forest technique. Proc Technol2016; 23: 187–192. Epub ahead of print 2016.
4.
HaoL. Improved GA-PSO algorithm for feature extraction of rolling bearing vibration signal. Paladyn2023; 14: 1–9. Epub ahead of print 2023. DOI: https://doi.org/10.1515/pjbr-2022-0092.
5.
SarkerIHAbusharkYBAlsolamiF, et al.IntruDTree: a machine learning based cyber security intrusion detection model. Symmetry (Basel)2020; 12: 1–15.
6.
MehtaAGoyalDChoudharyA, et al. Machine learning-based fault diagnosis of self-aligning bearings for rotating machinery using infrared thermography. Math Probl Eng; 2021; 2021: 1–15. Epub ahead of print 2021. DOI: https://doi.org/10.1155/2021/9947300
7.
SharmaAAmarnathMKankarPK. Feature extraction and fault severity classification in ball bearings. J Vib Control2016; 22: 176–192.
8.
MuralidharanVSugumaranV. Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump. Measurement (Lond)2013; 46: 353–359.
9.
AlshormanOIrfanMSaadN, et al. A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor. Shock Vib; 2020; 2020: 1–20. Epub ahead of print 2020. DOI: https://doi.org/10.1155/2020/8843759.
10.
AlShormanOAlkahatniFMasadehM, et al.Sounds and acoustic emission-based early fault diagnosis of induction motor: a review study. Adv Mech Eng2021; 13: 1–19.
11.
GlowaczATadeusiewiczRLegutkoS, et al.Fault diagnosis of angle grinders and electric impact drills using acoustic signals. Appl Acoust2021; 179: 108070.
12.
IshakSYawCTKohSP, et al. Fault classification system for switchgear cbm from an ultrasound analysis technique using extreme learning machine. Energies (Basel)2021; 14: 6279. Epub ahead of print 2021.
13.
VerellenTVerbelenFStockmanK, et al.Beamforming applied to ultrasound analysis in detection of bearing defects. Sensors2021; 21: 1–13.
14.
PradhanDMishraAK. Analysis of ISO VG 68 bearing oil for condition monitoring collected from an externally pressurized ball bearing system. Mater Today Proc2020; 44: 4602–4606.
15.
SunJWangLLiJ, et al.Online oil debris monitoring of rotating machinery: a detailed review of more than three decades. Mech Syst Signal Process2021; 149: 107341.
16.
LiYDuXWangX, et al.Industrial gearbox fault diagnosis based on multi-scale convolutional neural networks and thermal imaging. ISA Trans2022; 129: 309–320. Epub ahead of print 2022.
17.
LiYDuXWanF, et alRotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging. Chin J Aeronaut2020; 33: 427–438.
18.
KimSJKimKHwangT, et al.Motor-current-based electromagnetic interference de-noising method for rolling element bearing diagnosis using acoustic emission sensors. Measurement (Lond)2022; 193: 110912.
19.
CuiHGuanYChenH. Rolling element fault diagnosis based on VMD and sensitivity MCKD. IEEE Access2021; 9: 120297–120308.
20.
KumarAGandhiCPVashishthaG, et al. VMD Based trigonometric entropy measure: a simple and effective tool for dynamic degradation monitoring of rolling element bearing. Meas Sci Technol2022; 33: 014005. Epub ahead of print 2022.
21.
LiangMCaoPTangJ. Rolling bearing fault diagnosis based on feature fusion with parallel convolutional neural network. Int J Adv Manuf Technol2021; 112: 819–831.
22.
PanLZhaoLSongA, et al.Research on gear fault diagnosis based on feature fusion optimization and improved two hidden layer extreme learning machine. Measurement (Lond)2021; 177: 109317.
23.
Ribeiro JuniorRFdos Santos AreiasIAGomesGF. Fault detection and diagnosis using vibration signal analysis in frequency domain for electric motors considering different real fault types. Sens Rev2021; 41: 311–319.
24.
MakarovaIMukhametdinovEMavrinV, et al. Improvement of the vehicle clutch’s diagnosing system with the use of vibrodiagnostics. In 2018 IEEE international conference on technology management, operations and decisions, ICTMOD, 2018, pp.101–106.
25.
StrommengerDGühmannCKnoblichR, et al. Remaining lifetime prediction for reliability-based dry clutch control. In Proceedings of the 4th European conference of the prognostics and health management society (EPHM), 2018, vol. 4, pp.1–9.
26.
RamalingamSPrasadHVVPrakash RegallaS. Integrated prognostics observer for condition monitoring of an automated manual transmission dry clutch system. Int J Progn Health Manag2020; 8: 1–9.
BengioY. Learning deep architectures for AI. Found Trends Mach Learn2009; 2: 1–27.
29.
BengioYCourvilleA. Deep learning of representations. Intell Syst Ref Libr2013; 49: 1–28.
30.
LecunYBengioYHintonG. Deep learning. Nature2015; 521: 436–444.
31.
WangYGaoBChenH. Data-driven design of parity space-based FDI system for AMT vehicles. IEEE/ASME Trans Mechatron2015; 20: 405–415.
32.
Ostad-Ali-AskariKShayanM. Subsurface drain spacing in the unsteady conditions by HYDRUS-3D and artificial neural networks. Arab J Geosci2021; 14: 1–14. Epub ahead of print 2021. DOI: https://doi.org/10.1007/s12517-021-08336-0.
33.
VermaNGuptaRSevakulaR, et al. Signal transforms for feature extraction from vibration signal for air compressor monitoring. In IEEE Region 10 annual international conference, proceedings/TENCON, 2015. Epub ahead of print January 2015. DOI: https://doi.org/10.1109/TENCON.2014.7022275.
34.
MendesMFrancisRJuniorR, et al.Fault detection and diagnosis in electric motors using 1d convolutional neural networks with multi-channel vibration signals. Measurement2022; 190: 12–14.
35.
GuoXChenLShenC. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement (Lond)2016; 93: 490–502.
36.
ZhaoRYanRWangJ, et al.Learning to monitor machine health with convolutional bi-directional LSTM networks. Sensors (Switzerland)2017; 17: 1–18.
37.
BalazinskiMCzogalaEJemielniakK, et al.Tool condition monitoring using artificial intelligence methods. Eng Appl Artif Intell2002; 15: 73–80.
38.
LeeWJMendisGPSutherlandJW. Development of an intelligent tool condition monitoring system to identify manufacturing tradeoffs and optimal machining conditions. Procedia Manuf2019; 33: 256–263.
39.
ElangovanMDevasenapatiSBSakthivelNR, et al.Evaluation of expert system for condition monitoring of a single point cutting tool using principle component analysis and decision tree algorithm. Expert Syst Appl2011; 38: 4450–4459.
40.
ZhuYZhuCTanJ, et al.Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning. Renew Energy2022; 189: 90–103.
41.
JamilFVerstraetenTNowéA, et al.A deep boosted transfer learning method for wind turbine gearbox fault detection. Renew Energy2022; 197: 331–341.
42.
LiJLinMLiY, et al.Transfer learning network for nuclear power plant fault diagnosis with unlabeled data under varying operating conditions. Energy2022; 254: 124358.
43.
LiJLinMLiY, et al.Transfer learning with limited labeled data for fault diagnosis in nuclear power plants. Nucl Eng Des2022; 390: 111690.
44.
ZhongHLvYYuanR, et al.Neurocomputing bearing fault diagnosis using transfer learning and self-attention ensemble lightweight convolutional neural network. Neurocomputing2022; 501: 765–777.
45.
ZhiYMingKXuanZ, et al.Transfer learning method for bearing fault diagnosis based on fully convolutional conditional Wasserstein adversarial networks. Measurement (Mahwah N J)2021; 180: 109553.
46.
RuanDZhangFYanJ. Transfer learning between different working conditions on bearing fault diagnosis based on data augmentation. IFAC-PapersOnLine. 2021; 17: 1–18. Epub ahead of print 2021. DOI: https://doi.org/10.1016/j.ifacol.2021.08.141.
47.
HuoCJiangQShenY, et al.New transfer learning fault diagnosis method of rolling bearing based on ADC-CNN and LATL under variable conditions. Measurement (Mahwah N J)2022; 188: 110587.
48.
LiXJiangHXieM, et al.Advanced engineering informatics A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with multi-source domains. Adv Eng Inf2022; 51: 101480.
49.
ZhaoYChenY. Extreme learning machine based transfer learning for aero engine fault diagnosis. Aerosp Sci Technol2022; 121: 107311.
50.
ChakrapaniGSugumaranV. Transfer learning based fault diagnosis of automobile dry clutch system. Eng Appl Artif Intell2023; 117: 105522.
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