An Approach to Fault Diagnosis of Gearbox Based on an Instantaneous Angular Acceleration. Experimental Study
DOI:
https://doi.org/10.14529/ctcr200109Keywords:
diagnostics, accelerometer, wireless acceleration sensor, rotating shaft, rotating machinery, angular accelerationAbstract
Introduction. Gears are important parts of almost every operating mechanism in many industries. The gear condition monitoring is an important unit of condition monitoring of a mechanism as a whole. The vibration is one of the most used sources of information for equipment technical diagnostics. Traditionally, vibration is measured by accelerometers which are fixed on the mechanism body. Analyse of the measured data from the accelerometers requires applying the special methods and the staff with developed analytical skills. On the other hand, a novel approach to the accelerometer fitting location allows receiving extra diagnostic information and facilitating diagnosis. Aim. The present study shows the application of the novel approach to the accelerometer fitting location and the analyse of extra information for gearbox diagnosis. Materials and methods. The novel accelerometer fitting location is a rotating shaft of the mechanism. The extra diagnostic information is an angular acceleration of the rotating shaft. The theoretical base for the angular acceleration as diagnosis information is shown in the study. Results. The study contains experimental results of fault detecting such as the 'chipped tooth' and 'broken tooth' of a pinion. In addition, the study contains the proposed criteria for the detection of the local fault. Conclusion. The experimental results and the applied criteria show that the proposed approach allows detecting the pinion local defect on the first pinion rotation frequency clearly at various rotation frequencies.
References
Lei Y., Li N., Lin J., He Z. Two New Features for Condition Monitoring and Fault Diagnosis of Planetary Gearboxes. JVC/Journal of Vibration and Control, 2015, vol. 21, no. 4, pp. 755–764. DOI: 10.1177/1077546313486284
Nie M., Wang L. Review of Condition Monitoring and Fault Diagnosis Technologies for Wind Turbine Gearbox. Procedia CIRP Elsevier, 2013, 11, pp. 287–290. DOI: 10.1016/j.procir.2013.07.018
Genkin M.D., Sokolova A.G. Vibroakusticheskaya diagnostika mashin i mexanizmov [Vibroacoustic Diagnostics of Machines and Images]. Moscow, Engineering Publ., 1987, 288 p.
Rincon A.F., Viadero F., Iglesias M., de-Juan A., Garcia P., Sancibrian R. Effect of Cracks and Pitting Defects on Gear Meshing. Proceedings of the Institute of Mechanical Engineers C, 2012, 226 (11), pp. 2805–2815. DOI: 10.1177/0954406212437104
McFadden P.D. Detecting Fatigue Cracks in Gears by Amplitude and Phase Demodulation of the Meshing Vibration. Journal of Vibration, Acoustics, Stress, and Reliability in Design, 1986, 108, pp. 165–170. DOI: 10.1115/1.3269317
Li C., Sanchez R., Zurita G., Cerrada M., Cabrera D., Vásquez R.E. Gearbox Fault Diagnosis Based on Deep Random Forest Fusion of Acoustic and Vibratory Signals. Mechanical Systems and Signal Processing, 2016, vol. 76–77, pp. 283–293. DOI: 10.1016/j.ymssp.2016.02.007
Loutridis S.J. Gear Failure Prediction using Multi-scale Local Statistics. Engineering Structures, 2008, 30, pp. 1214–1223. DOI: 10.1016/j.engstruct.2007.07.022
Samuel P.D., Pines D.J. A Review of Vibration-based Techniques for Helicopter Transmission Diagnostics. Journal of Sound and Vibration, 2005, 282, pp. 475–508. DOI: 10.1016/j.jsv.2004.02.058
Lei Y.G., Lin J., Zou M.J., He Z.J. Condition Monitoring and Fault Diagnosis of Planetary Gearboxes: a Review. Measurement, 2014, 48, pp. 292–305. DOI: 10.1016/j.measurement.2013.11.012
Zakrajsek J.J., Lewicki D.G. Detecting Gear Tooth Fatigue Cracks in Advance of Complete Fracture. Tribotest, 1998, vol. 4, iss. 4, pp. 407–422.
Antolick L.J., Branning J.S., Wade D.R., and Dempsey P.J. Evaluation of Gear Condition Indicator Performance on Rotorcraft Fleet. AHS 66th Annual Forum and Technology Display, Phoenix, United States, NASA, 2010, May, p. 12.
Zhang X.H., Kang J.S., Bechhoefer E., Zhao J. A New Feature Extraction Method for Gear Fault Diagnosis and Prognosis. Eksploatacja i Niezawodnosc – Maintenance Reliability, 2014, 16 (2), pp. 295–300.
Antoni J. The Spectral Kurtosis: a Useful Tool for Characterising Nonstationary Signals. Mechanical Systems and Signal Processing, 2006, vol. 20, no. 2, pp. 282–307. DOI: 10.1016/j.ymssp.2004.09.001
Combet F., Gelman L. Optimal Filtering of Signals for Early Damage Detection Based on the Spectral Kurtosis. Mechanical Systems and Signal Processing, 2009, 23, pp. 652–668. DOI: 10.1016/j.ymssp.2008.08.002
Barszcz T., Randall R.B. Application of Spectral Kurtosis for Detection of a Tooth Crack in a Planetary Gear of a Wind Turbine. Mechanical Systems and Signal Processing, 2009, 23, pp. 1352–1365. DOI: 10.1016/j.ymssp.2008.07.019
Liu H., Huang W., Wang S., Zhu Z. Adaptive Spectral Kurtosis Filtering Based on Morlet Wavelet and its Application for Signal Transients Detection. Signal Processing, 2014, 96, pp. 118–124. DOI: 10.1016/j.sigpro.2013.05.013
Feldman Michael. Hilbert Transform in Vibration Analysis. Mechanical Systems and Signal Processing, 2011, vol. 25, no. 3, pp. 735–802. DOI: 10.1016/j.ymssp.2010.07.018
Djebala A., Ouelaa N., Benchaabane C. Application of theWavelet Multi-resolution Analysis and Hilbert Transform for the Prediction of Gear Tooth Defects. Meccanica, 2012, Oct., vol. 47, no. 7, pp. 1601–1612. DOI: 10.1007/s11012-012-9538-1
Tian X. Dynamic Simulation for System Response of Gearbox Including Localized Gear Faults. Dissertation of Master Degree, Edmonton, Canada, University of Alberta, 2004, p. 194.
Sinitsin V.V., Semenov A.S., Shestakov A.L. Ustrojstvo izmerenija vibrouskorenija podvizhnyh jelementov mashin i mehanizmov [Acceleration Measuring Device Moving Parts of Machines and Mechanisms]. Patent RU 142934 U1, Bull. no. 19, Publ. 10.07.2014.
Sinitsin V.V., Shestakov A.L. Wireless Acceleration Sensor of Moving Elements for Condition Monitoring of Mechanisms. Measurement Science and Technology, 2017, Aug., vol. 28, no. 9, p. 094002. DOI: 10.1088/1361-6501/aa7ab6
Henry M., Sinitsin V.V. Prism Signal Processing for Machine Condition Monitoring I: Design and Simulation. IEEE Industrial Cyber-Physical Systems (ICPS), St. Petersburg, Russia, IEEE, 2018, May, pp. 452–457. DOI: 10.1109/ICPHYS.2018.8390747
Henry M., Sinitsin V.V. Prism Signal Processing for Machine Condition Monitoring II: Experimental Data and Fault Detection. IEEE Industrial Cyber-Physical Systems (ICPS), St. Petersburg, Russia, IEEE, 2018, May, pp. 458–463. DOI: 10.1109/ICPHYS.2018.8390748
