Data driven modeling and estimation of accumulated damage in mining vehicles using on-board sensors



Published Oct 2, 2017
Erik Jakobsson Erik Frisk Robert Pettersson Mattias Krysander


The life and condition of a MT65 mine truck frame is to a large extent related to how the machine is used. Damage from different stress cycles in the frame are 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 used. To make a monitoring system cheap and robust enough for a mining application, a small number of robust sensors are preferred rather than a multitude of local sensors such as strain gauges. The main question to be answered is whether a low number of robust on-board sensors can give the required information to recreate stress signals at various locations of the frame. Also the choice of sensors among many different locations and kinds are considered. A final question is whether the data could also be used to estimate road condition. By using accelerometer, gyroscope and strain gauge data from field tests of an Atlas Copco MT65 mine truck, coherence and Lassoregression were evaluated as means to select which signals to use. ARX-models for stress estimation were created using the same data. By simulating stress signals using the models, rain flow counting and damage accumulation calculations were performed. The results showed that a low number of on-board sensors like accelerometers and gyroscopes could give enough information to recreate some of the stress signals
measured. Together with a linear model, the estimated stress was accurate enough to evaluate the accumulated fatigue damage in a mining truck. The accumulated damage was also used to estimate the condition of the road on which the truck was traveling. To make a useful road monitoring system some more work is required, in particular regarding how vehicle speed influences damage accumulation.

How to Cite

Jakobsson, E., Frisk, E., Pettersson, R., & Krysander, M. (2017). Data driven modeling and estimation of accumulated damage in mining vehicles using on-board sensors. Annual Conference of the PHM Society, 9(1).
Abstract 325 | PDF Downloads 177



condition monitoring, system identification, damage accumulation

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.
Heine, R., & Barker, D. (2007). Simplified terrain identification and component fatigue damage estimation model for use in a health and usage monitoring system. Microelectronics Reliability, 47(12), 1882 - 1888.
Palmgren, A. (1924). Die lebensdauer von kugellagern. Zeitschrift des Vereins Deutscher Ingenieure, 68(14), 339–341.
Qian, J., Hastie, T., Friedman, J., Tibshirani, R., & Simon, N. (2013). Glmnet for matlab.
Rupp, A. N. J., Masieri, A., & Dornbusch, T. (2005, 11). Efficient monitoring of loads and stresses under service and test conditions. In Sae technical paper. SAE International.
Stephens, R., Fatemi, A., Stephens, R., & Fuchs, H. (2000). Metal fatigue in engineering. John Wiley & Sons.
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso (Vol. 58) (No. 1).
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