Belt drive contamination is considered one of the most common failure modes that could be developed in the belts due to harsh operation conditions, high humidity, and sunlight exposure, reducing the belt’s performance. If the belt failure has not been detected early, a sudden shutdown may happen, producing safety and economic consequences. However, most maintenance personnel use their senses of sight, hearing, smell, and touch to identify the cause of the problem while diagnosing a belt drive condition. Hence, this research involves developing an intelligent contamination status detection system based on vibration signal analysis for a pulley-belt rotating system. Time-domain signal analysis was employed to extract some suggestive features such as the root mean square, kurtosis, and skewness from the vibration data. An artificial neural network (ANN) model was built to detect the simulated different operating conditions. The vibration data was gathered with the help of two MEMS accelerometers (ADXL335) interfaced with an NI USB-6009 data acquisition device. A signal capture, analysis, and feature extraction system was developed using Matlab Simulink. The simulated operating conditions include clean, wet, and powder-contaminated belts. The results showed that the designed system could identify the pulley-belt operation conditions with 100% overall accuracy.
HuY., YanY., WangL. and QianX., Non-contact vibration monitoring of power transmission belts through electrostatic sensing, IEEE Sensors Journal16 (2016), 3541–3550. https://doi.org/10.1109/JSEN.2016.2530159.
2.
HyunD., KangT.-J. and KimJ., Fault Diagnosis of Belt Driving Power Transfer Facility Using Vibration Analysis, Journal of Electrical Engineering Technology16 (2021), 2257–2264. https://doi.org/10.1007/s42835-021-00711-x.
3.
JaberA.A. and AliK.M., Artificial neural network based fault diagnosis of a pulley-belt rotating system, International Journal on Advanced Science, Engineering and Information Technology9 (2019), 544–551. https://dx.doi.org/10.18517/ijaseit.9.2.7581.
4.
SchiewaldtK., LucasG., RochaM., FragaC. and AndreoliA., Identification of Stator Winding Insulation Faults in Three-Phase Induction Motors UsingMEMSAccelerometers, In Proceedings, vol. 42, no. 1, p. 66, MDPI, 2020. https://doi.org/10.3390/ecsa-6-06630.
5.
KhazaeeM., BanakarA., GhobadianB., MirsalimM.A. and MinaeiS., Remaining useful life (RUL) prediction of internal combustion engine timing belt based on vibration signals and artificial neural network, Neural Computing and Applications33 (2021), 7785–7801. https://doi.org/10.1007/s00521-020-05520-3.
6.
Narendiranath BabuT., SenthilnathanN., PancholiS., Nikhil KumarS.P., Rama PrabhaD., MohammedN., et al., Fault analysis on continuous variable transmission using DB-06 wavelet decomposition and fault classification using ANN, Journal of Intelligent and Fuzzy Systems41 (2021), 1297–1307. https://doi.org/10.3233/JIFS-210199.
7.
PicotA., FournierE., RégnierJ., TientcheuYamdeuM., AndréjakJ. and MaussionP., Statistic-Based Method to Monitor Belt Transmission Looseness Through Motor Phase Currents, IEEE Transactions on Industrial Informatics13 (2017), 1332–1340. https://doi.org/10.1109/TII.2017.2661317.
8.
LiW., WangZ., ZhuZ., ZhouG. and ChenG., Design of online monitoring and fault diagnosis system for belt conveyors based on wavelet packet decomposition and support vector machine, Advances in Mechanical Engineering5 (2013), 797183. https://dx.doi.org/10.1155/2013/797183.
9.
LiX., LiY., ZhangY., LiuF. and FangY., Fault Diagnosis of Belt Conveyor Based on Support Vector Machine and Grey Wolf Optimization, Mathematical Problems in Engineering2020 (2020). https://doi.org/10.1155/2020/1367078.
10.
PangY. and LodewijksG., Large-scale conveyor belt system maintenance decision-making by using fuzzy causal modeling, In Proceedings, 2005 IEEE Intelligent Transportation Systems, pp. 563–567, 2005. https://doi.org/10.1109/ITSC.2005.1520082.
11.
YuanY., MengW. and SunX., Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network, The Open Mechanical Engineering Journal8 (2014). https://dx.doi.org/10.2174/1874155X01408010916.
12.
PollakA., TemichS., PtasińskiW., KucharczykJ. and GasiorekD., Prediction of belt drive faults in case of predictive maintenance in industry 4.0 platform, Applied Sciences (Switzerland)11 (2021). https://doi.org/10.3390/app112110307.
13.
AltafM., AkramT., KhanM.A., IqbalM., ChM.M.I. and HsuC.H., A New Statistical Features Based Approach for Bearing Fault Diagnosis Using Vibration Signals, Sensors22 (2022). https://doi.org/10.3390/s22052012.
14.
KumarS., KolekarT., PatilS., BongaleA., KotechaK., ZaguiaA., et al., A Low-Cost Multi-Sensor Data Acquisition System for Fault Detection in Fused Deposition Modelling, Sensors22 (2022), 517. https://doi.org/10.3390/s22020517.
15.
BarmanJ. and HazarikaD., Linear and Quadratic Time–Frequency Analysis of Vibration for Fault Detection and Identification of NFR Trains, IEEE Transactions on Instrumentation and Measurement69(11) (2020), 8902–8909. https://doi.org/10.1109/TIM.2020.2998888.
16.
ShankarN.V.S., JitendraK.V., Ravi ShankarH. and ManikumarM., Low-Cost Test Rig for Demonstrating Single Plane Balancing Using Vibrations, in Advances in Applied Mechanical Engineering, Singapore, 2020, pp. 433–441: Springer Singapore. https://dx.doi.org/10.1007/978-981-15-1201-8_49.
17.
PandeyB., JhaB.K. and MahtoS., Online Tool Wear Monitoring Using Low-Cost Data Acquisition System and LabVIEW™ Program, in Modeling, Simulation and Optimization, Singapore, 2021, pp. 87–99: Springer Singapore. https://dx.doi.org/10.1007/978-981-15-9829-6_7.
18.
Al-HaddadL.A. and JaberA.A., An Intelligent Fault Diagnosis Approach for Multirotor UAVs Based on Deep Neural Network of Multi-Resolution Transform Features, Drones, Article7(2) (2023), Art. no. 82. https://doi.org/10.3390/drones7020082.
19.
VojtkoI., BaronP., PollakM. and KaščakJ., Examining the Effect of Alignment of the Rotor of the Emissions Exhaust Fan on Its Operating Parameters, Advances in Materials Science and Engineering2019 (2019), 4985395. https://dx.doi.org/10.1155/2019/4985395.
20.
WangX.C., HuangB., WangR.L., MoJ.L. and OuyangH., Friction-induced stick-slip vibration and its experimental validation, Mechanical Systems and Signal Processing142 (2020), 106705. https://dx.doi.org/10.1016/j.ymssp.2020.106705.
21.
ZhuH., ZhuW.D. and FanW., Stick–slip oscillations of an engine front-end accessory drive system with a mechanical tensioner, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering235 (2021), 400–416. https://dx.doi.org/10.1177/0954407020950581.
22.
LiY., WangJ., ShanY., WangC. and HuY., Measurement and analysis of downhole drill string vibration signal, Applied Sciences (Switzerland), Article11(23) (2021), Art. no. 11484. https://doi.org/10.3390/app112311484.
23.
RiazS., ElahiH., JavaidK. and ShahzadT.J.A.P.J.o.M.R., Vibration feature extraction and analysis for fault diagnosis of rotating machinery-a literature survey, Asia Pacific Journal of Multidisciplinary Research5(1) (2017), 103–110.
24.
StraczkiewiczM. and BarszczT., Application of Artificial Neural Network for Damage Detection in Planetary Gearbox of Wind Turbine, Shock and Vibration2016 (2015), 4086324. https://doi.org/10.1155/2016/4086324.
25.
JaberA.A. and BickerR., Fault diagnosis of industrial robot bearings based on discrete wavelet transform and artificial neural network, International Journal of Prognostics and Health Management7 (2016). https://dx.doi.org/10.36001/ijphm.2016.v7i2.2365.
26.
Babu RaoK. and Mallikarjuna ReddyD., Fault detection in rotor system by discrete wavelet neural network algorithm, JVC/Journal of Vibration and Control (2021). https://doi.org/10.1177/10775463211030754.
27.
Kara Mostefa KhelilC., AmroucheB., KaraK. and ChouderA., The impact of the ANN’s choice on PV systems diagnosis quality, Energy Conversion and Management240 (2021), 114278. https://doi.org/10.1016/j.enconman.2021.114278.
28.
RuslanR., LaomeL., UsmanI. and HarisaE.W., Electricity Consumption Modelling in Kendari using the Backpropagation Method on the Artificial Neural Network, Journal of Physics: Conference Series1863(1), IOP Publishing 2021. https://doi.org/10.1088/1742-6596/1863/1/012076.
29.
SharmaA., JigyasuR., MathewL. and ChatterjiS., Bearing Fault Diagnosis Using Frequency Domain Features and Artificial Neural Networks, Singapore, 2019, pp. 539-547. https://dx.doi.org/10.1007/978-981-13-1747-7_52.
30.
BaptistaF.D., RodriguesS. and Morgado-DiasF., Performance comparison of ANN training algorithms for classification, in 2013 IEEE 8th International Symposium on Intelligent Signal Processing, 2013, pp. 115–120.https://dx.doi.org/10.1109/WISP.2013.6657493
31.
JaiswalP., GuptaN.K. and AmbikapathyA., Comparative study of various training algorithms of artificial neural network, in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2018, pp. 1097–1101. https://doi.org/10.1109/ICACCCN.2018.8748660.
32.
LuY.J. and WangC.H., Integration of wavelet decomposition and artificial neural network for failure prognosis of reciprocating compressors, Process Safety Progress40 (2021), 105–115. https://doi.org/10.1002/prs.12239.
33.
KlineD.M. and BerardiV.L., Revisiting squared-error and cross-entropy functions for training neural network classifiers, Neural Computing Applications14 (2005), 310–318. https://doi.org/10.1007/s00521-005-0467-y.
34.
Ribeiro JuniorR.F., de AlmeidaF.A. and GomesG.F., Fault classification in three-phase motors based on vibration signal analysis and artificial neural networks, Neural Computing and Applications32 (2020), 15171–15189. https://doi.org/10.1007/s00521-020-04868-w.
35.
AbdelrhmanA.M., YingL., AliY.H., AhmadI., GeorgantopoulouC.G., NorF.M., et al., Diagnosis model for bearing faults in rotating machinery by using vibration signals and binary logistic regression, AIP Conference Proceedings Vol. 2262. No. 1. AIP Publishing LLC, 2020. https://doi.org/10.1063/5.0017221.
36.
LiuX., PeiD., LodewijksG., ZhaoZ. and MeiJ., Acoustic signal based fault detection on belt conveyor idlers using machine learning, Advanced Powder Technology, Article, 31(7) (2020), 2689–2698. https://doi.org/10.1016/j.apt.2020.04.034.
