Vibration-based Condition Monitoring of Heavy Duty Machine Driveline Parts: Torque Converter, Gearbox, Axles and Bearings



Published Jun 1, 2019
K¨allstr¨om Elisabeth Lindstr¨om John H°akansson Lars Karlberg Magnus Lin Jing


As more features are added to the heavy-duty construction equipment, its complexity increases and early fault detection of certain components becomes more challenging due to too many fault codes generated when a failure occurs. Hence, there is a need to complement the present onboard diagnostics method with a more reliable diagnostics method for adequate condition monitoring of the heavy-duty construction equipment in order to improve uptime. Major components of the driveline (such as the gearbox, torque converter, bearings and axles) are components necessary to monitor. A failure among any of these major components of the driveline may result in the machine standing still until a repair is scheduled. In this paper, vibration based condition monitoring methods are presented with the purpose to provide a diagnostic framework possible to implement onboard for monitoring of critical driveline parts in order to reduce service cost and improve uptime. For the development of this diagnostic framework, sensor data from the gearbox, torque converter, bearings and axles are considered. Further, the feature extraction of the data collected has been carried out using adequate signal processing methods, which includes: Adaptive Line Enhancer and Order Power Spectrum respectively. In addition, Bayesian learning was utilized for adaptive learning of the extracted features for deviation detection. Bayesian learning is a powerful prediction method as it combines the prior information with knowledge measured to make updates. The results indicate that the vibration properties of the gearbox, torque converter, bearings and axle are relevant for early fault detection of the driveline. Furthermore, vibration provides information about the internal features of these components for detecting deviations from normal behavior. In this way, the developed methods may be implemented onboard for the continuous monitoring of these critical driveline parts of the heavy-duty construction equipment. Thus, if their health starts to degrade a service and/or repair may be scheduled well in advance of a potential failure and in that way the downtime of a machine may be reduced and costly replacements and repairs avoided

Abstract 263 | PDF Downloads 177



bearings, Order Analysis, gearbox, Adaptive filtering, Automatic Transmission, Adaptive Line Enhancer, Axle, Bayesian Learning, Order Power Spectrum, Torque Converter and Vibration

Abdusslam, S., Gu, F., & Ball, A. (2009). Bearing fault diagnosis based on vibration signals. In Proceedings of computing and engineering annual researchers’ conference (p. 1350-1359).
Andren, L., H°akansson, L., Brandt, A., & Claesson, I. (1978). Identification of dynamic properties of boring bar vibrations in a continuous boring operation. Mechanical Systems and Signal Processing, 18(4), 869-901.
Bendat, J., & Piersol, A. (Eds.). (2000). Random data; analysis and measurement procedures, third edition. John Wiley & Sons.
Brandt, A. (Ed.). (2011). Noise and vibration analysis: Signal analysis and experimental procedures. John Wiley & Sons.
Brkovic, A., Gajic, D., Gligorijevic, J., Savic-Gajic, I., Georgieva, O., & S., D. G. (2017). Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery. Energy, 136(1), 63-71.
Fan, X., & Zuo, M. J. (2006). Gearbox fault detection using hilbert and wavelet packet transform. Mechanical Systems and Signal Processing, 20, 966-982.
Gelman, A., Carlin, J., Stern, H., Dunson, D., Vehtari, A., & Rubin, D. (Eds.). (2014). Bayesian data analysis, third edition. ISBN 978-1-4398-4095-5, CRC Press.
Giannakis, G. (Ed.). (1999). Cyclostationary signal analysis. Digital Signal Processing Handbook ,CRC Press LLC.
Guo, Y., Wu, X., Na, J., & Fung, R. (2016). Envelope synchronous average scheme for multi-axis gear fault detection. Sound and Vibration, 365, 276-286.
Hamada, M. S., Wilson, A., Reese, C., & Martz, H. (Eds.). (2008). Bayesian reliability. ISBN 978-0-387-77948-5, Springer.
Harris, F. (1978). On the use of windows for harmonic analysis with the discrete fourier transform. IEEE, 66(6), 70–73.
Haykin, S. (Ed.). (2014). Adaptive filter theory, (fifth edition). ISBN 978-0-132-67145-3, Pearson Education Limited.
He, D., & Li, R. (2011). A new vibration signal processing method for fault detection. Mechanical Systems and Signal Processing.
Ho, D., & Randall, R. (1997). Effects of time delay, order of fir filter and convergence factor on self adaptive noise cancellation. In Proceedings of the fifth international congress on sound and vibration.
Hong, L., & Dhupia, J. (2014). A time domain approach to gearbox fault based on measured vibration signals. Mechanical Systems and Signal Processing, 333, 2164-2180.
Huibin, L., Mengxi, N., Chengxia, Z., & Bo, Y. (2012). Experimental study on the noise identification of the rear driving axle. In Proceedings of international conference on intelligent systems design and engineering application (p. 1255-1258).
Immovilli, F., Bellini, A., Rubini, R., & C., T. (2010). Diagnosis of bearing faults in induction machines by vibration or current signals: A critical comparison. In Ieee transactions on industry application (Vol. 46, p. 1350-1359).
Junsheng, C., Yu, Y., & Dejie, Y. (2010). The envelope order spectrum based on generalized demodulation timefrequency analysis and its application to gear fault diagnosis. Mechanical System and Signal Processing, 24(2), 508-521.
K¨allst¨om, E., Lindstr¨om, J., H°akansson, L., Karlberg, M., & O¨ berg, O. (2017). Identification of vibration properties of wheel loader driveline parts as a base for adequate condition monitoring: Bearings. In 24th international congress on sound and vibration, london, united kingdom, 23-27 july, 2017.
Kruschke, J. K. (Ed.). (2015). Doing bayesian data analysis. ISBN 978-0-12-405888-0, Academic Press.
Li, H., & Ai, S. (2008). Application of order bi-cepstrum to gearbox fault detection. In Proceedings of the 7th world congress on intelligent control and automation (p. 1781-1785).
Lindstr¨om, J., Plankina, K., D.and Nilsson, Parida, H., V.and Ylinen¨a¨a, & Karlsson, L. (2013). Functional products: Business model elements. In 5th cirp international conference on industrial product-service systems, bochum, germany (p. 1781-1785).
Meier, H., Roy, R., & Seliger, G. (2008). Industrial productservice systems. IPS2, CIRP Annals Manufacturing Technology(4), 1-24.
Proakis, J. G., & Manolakis, D. G. (Eds.). (2006). Digital signal processing: Principles, algorithms, and applications (fourth edition). ISBN:0131873741, Prentice-Hall, Inc. Upper Saddle River, NJ, USA.
Ramli, R., Noor, A., & Samad, S. (2012). A review of adaptive line enhancers for noise cancellation. Australian Journal of Basic and Applied Sciences, 6(6), 337-352.
Randall, R. (Ed.). (n.d.). John Wiley & Sons, Hoboken, NJ.
Randall, R. (2004a). State of the art in monitoring rotating machinery: Part 1. Sound and Vibration, 14-20.
Randall, R. (2004b). State of the art in monitoring rotating machinery: Part 2. Sound and Vibration, 14-20.
Randall, R., & Antoni, J. (2011). Rolling element bearing diagnostics-a tutorial. Mechanical System and Signal Processing, 25(2), 485-520.
Randall, R., Sawalhi, N., & Coats, M. (2011). A comparison of methods for separation of determinstic and random signals. Mechanical System and Signal Processing, 1(1), 11-19.
Santacruz, M., & Félix, M. (2014). Test point optimization process for a real-time vibration monitoring system on a differential axle fixed rig. In Proceedings of ieee (p. 1255-1258).
Sawalhi, N., & Randall, R. (2008). Simulating gear and bearing interactions in the presence of faults: Part i. the combined gear bearing dynamic model and the simulation of localised bearing faults. Mechanical Systems and Signal Processing, 22, 1924-1951.
Shao, Y., Liang, J., Gu, F., Chen, Z., & Ball, A. (2011). Fault prognosis and diagnosis of an automotive rear axle gear using a rbf-bp neural network. 9th International Conference on Damage Assessment of Structures (DAMAS 2011) IOP Publishing, Journal of Physics: Conference Series.
Tuma, J. (Ed.). (2014). Vehicle gearbox noise and vibration: Measurement, signal analysis, signal processing and noise reduction measures. John Wiley & Sons.
Welch, P. D. (1967). The use of fast fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. IEEE Transactions on Audio and Electroacoustics(6), 70–73.
Widrow, B., & Stearns, S. D. (Eds.). (1985). Adaptive signal processing. ISBN 0-13-004029-0, Prentice-Hall International (UK).
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