The life and condition of a mine truck frame are related to how the machine is used. Damage from stress cycles is accumulated over time, and measurements throughout the life of the machine are needed to monitor the condition. This results in high demands on the durability of sensors, especially in a harsh mining application. To make a monitoring system cheap and robust, sensors already available on the vehicles are preferred rather than additional strain gauges. The main question in this work is whether the existing on-board sensors can give the required information to estimate stress signals and calculate accumulated damage of the frame. Model complexity requirements and sensors selection are also considered. A final question is whether the accumulated damage can be used for prognostics and to increase reliability. The investigation is performed using a large data set from two vehicles operating in real mine applications. Coherence analysis, ARX-models, and rain flow counting are techniques used. The results show that a low number of available on-board sensors like load cells, damper cylinder positions, and angle transducers can give enough information to recreate some of the stress signals measured. The models are also used to show significant differences in usage by different operators, and its effect on the accumulated damage.
Fatigue Prognosis, Wind Turbine, Physics-informed Machine Learning
ASTM E 1049-85 (Reapproved 1997). (1999). Standard practices for cycle counting in fatigue analysis (Vol. Vol. 03.01; Tech. Rep.). Philadelphia.
Byggavdelningen, B., Göransson, L., & Åkerlund, S. (1999). Boverkets handbok om stålkonstruktioner - bsk 99. Boverket.
Gurgenci, H., & Guan, Z. (2001). Mobile plant maintenance and the dutymeter concept. Journal of Quality in Maintenance Engineering, 7(4), 275-286.
Jakobsson, E., Frisk, E., Pettersson, R., & Krysander, M. (2017). Data driven modeling and estimation of accumulated damage in mining vehicles using on-board sensors. PHM Society.
Japkowicz, N., & Stephen, S. (2002). The class imbalance problem: A systematic study. Intelligent data analysis, 6(5), 429–449.
Koistinen, A. H., & Juuso, E. K. (2015). Stress monitoring of underground load haul dumper front axle with intelligent indices. In 4th workshop on mining, mineral and metal processing (Vol. 3000, pp. 25–27).
Ljung, L. (Ed.). (1999). System identification (2nd ed.): Theory for the user. Upper Saddle River, NJ, USA: Prentice Hall PTR.
Molent, L., & Aktepe, B. (2000). Review of fatigue monitoring of agile military aircraft. Fatigue & Fracture of Engineering Materials & Structures, 23(9), 767–785.
Molent, L., Barter, S., & Foster, W. (2012). Verification of an individual aircraft fatigue monitoring system. International Journal of Fatigue, 43, 128–133.
Musallam, M., & Johnson, C. M. (2012, Dec). An efficient implementation of the rainflow counting algorithm for life consumption estimation. IEEE Transactions on Reliability, 61(4), 978-986.
Newland, D. E. (2012). An introduction to random vibrations, spectral & wavelet analysis. Courier Corporation.
Pais, M. J., & Kim, N. H. (2015). Predicting fatigue crack growth under variable amplitude loadings with usage monitoring data. Advances in Mechanical Engineering, 7(12), 1687814015619135.
Palmgren, A. (1924). Die lebensdauer von kugellagern. Zeitschrift des Vereins Deutscher Ingenieure, 68(14), 339–341.
Staszewski,W., Boller, C., & Tomlinson, G. R. (2004). Health monitoring of aerospace structures: smart sensor technologies and signal processing. John Wiley & Sons.
Stephens, R., Fatemi, A., Stephens, R., & Fuchs, H. (2000). Metal fatigue in engineering. John Wiley & Sons.
Zio, E. (2016). Some challenges and opportunities in reliability engineering. IEEE Transactions on Reliability,