Condition Based Monitoring for a Hydraulic Actuator



Stephen Adams Peter A. Beling Kevin Farinholt Nathan Brown Sherwood Polter Qing Dong


In some environments where prognostics and health management would be beneficial, for example on board U.S. naval vessels, installation location and accessibility to power system must be considered. In this study, we investigate condition based maintenance and fault diagnosis for hydraulic actuators in power constrained environments. The experimental setup for collecting data is outlined, and a data set replicating multiple types of faults is collected. Several types of machine learning classifiers, including random forest and classification trees, are tested on the data set. Prediction accuracy as well as training and testing times are compared, which are used as a surrogate for power consumption in this study. We find that the random forest algorithm provides the lowest error rate of the tested classifiers but has some of the highest training and testing times. Classification trees, on the other hand, provide a better tradeoff between accuracy and computation time.

How to Cite

Adams, S., Beling, P. A., Farinholt, K., Brown, N., Polter, S., & Dong, Q. (2016). Condition Based Monitoring for a Hydraulic Actuator. Annual Conference of the PHM Society, 8(1).
Abstract 18 | PDF Downloads 17



Condition Based Maintenance, fault diagnostics, hydraulic actuator, power constraints

Barty´s, M., & de las Heras, S. (2003). Actuator simulation of the DAMADICS benchmark actuator system. Safe-Process 2003, Washington DC.
Barty´s, M., & Ko´scielny, J. M. (2002). Application of fuzzy logic fault isolation methods for actuator diagnosis. In Proc. 15th IFAC World Congress, Barcelona, Spain (pp. 21–26).
Barty´s, M., Patton, R., Syfert, M., de las Heras, S., & Quevedo, J. (2006). Introduction to the damadics actuator FDI benchmark study. Control Engineering Practice, 14(6), 577–596.
Bishop, C. (2006). Pattern recognition and machine learning. Springer-Verlag New York.
Breiman, L. (2001). Random forests. Machine learning, 45(1), 5–32.
Duda, R. O., Hart, P. E., & Stork, D. G. (2001). Pattern classification. 2nd. Edition. New York.
Grzes, M., Poupart, P., Yang, X., & Hoey, J. (2015). Energy efficient execution of POMDP policies. Cybernetics, IEEE Transactions on, 45(11), 2484–2497.
Murphy, K. P. (2012). Machine learning: a probabilistic perspective. MIT press.
Patan, K., & Parisini, T. (2002). Stochastic approaches to dynamic neural network training. actuator fault diagnosis study. In Proc. 15th IFAC Triennial World Congress, b (Vol. 2, pp. 21–26).
Previdi, F., & Parisini, T. (2006). Model-free actuator fault detection using a spectral estimation approach: the case of the DAMADICS benchmark problem. Control Engineering Practice, 14(6), 635–644.
Puig, V., Quevedo, J., Staucu, A., Lunze, J., Neidig, J., Planchon, P., & Supavatanakul, P. (2003). Comparison of interval models and quantised systems in fault detection with application to the DAMADICS actuator benchmark problem. In 5th IFAC Symposium on Fault Detection. Supervision and Safety for Technical Processes (SAFEPROCESS-2003), Washington, USA.
Puig, V., Stancu, A., Escobet, T., Nejjari, F., Quevedo, J., & Patton, R. J. (2006). Passive robust fault detection using interval observers: Application to the DAMADICS benchmark problem. Control engineering practice, 14(6), 621–633.
Uppal, F. J., Patton, R. J., & Witczak, M. (2006). A neurofuzzy multiple-model observer approach to robust fault diagnosis based on the DAMADICS benchmark problem. Control Engineering Practice, 14(6), 699–717.
Wang, Y., Lin, J., Annavaram, M., Jacobson, Q. A., Hong, J., Krishnamachari, B., & Sadeh, N. (2009). A framework of energy efficient mobile sensing for automatic user state recognition. In Proceedings of the 7th international conference on Mobile systems, applications, and services (pp. 179–192).
Weimer, J., Ahmadi, S. A., Araujo, J., Mele, F. M., Papale, D., Shames, I., . . . Johansson, K. H. (2012). Active actuator fault detection and diagnostics in hvac systems. In Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings (pp. 107–114).
Witczak, M., Korbicz, J., Mrugalski, M., & Patton, R. J. (2006). A GMDH neural network-based approach to robust fault diagnosis: application to the DAMADICS benchmark problem. Control Engineering Practice, 14(6), 671–683.
Yan, Z., Subbaraju, V., Chakraborty, D., Misra, A., & Aberer, K. (2012). Energy-efficient continuous activity recognition on mobile phones: An activity-adaptive approach. In Wearable Computers (ISWC), 2012 16th International Symposium on (pp. 17–24).
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