A Hydraulic Actuator Condition Monitoring Dataset for Machine Learning
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Published
Feb 28, 2026
Stephen Adams
Floyd Steele
Nate Brown
Kevin Farinholt
Peter Beling
Sherwood Polter
Abstract
Hydraulic actuators are used across the shipping industry and are installed in various environments performing a wide array of operations. Their widespread use necessitates a condition-based maintenance program, ideally utilizing machine learning models to provide real-time estimates of the health condition, to maintain an acceptable level of fleet readiness. This paper presents a hydraulic actuator dataset collected from six industrial actuators under various fault conditions and loads. The dataset is publicly available for use in other studies. This paper describes the actuator test stand and provides a series of baseline machine learning experiments. The numerical experiments demonstrate that a machine learning model can identify classes across a range of machine learning problems. However, these numerical experiments use data from each actuator during training. The final numerical experiment withholds data from individual actuators and fault types during training. The model is unable to correctly classify the withheld fault type during this experiment. This presents an opportunity for the data set to be used in further research on generalizing models.
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Keywords
actuator, dataset, machine learning
References
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Adams, S., Beling, P. A., Farinholt, K., Brown, N., Polter, S., & Dong, Q. (2016). Condition based monitoring for a hydraulic actuator. In Annual conference of the phm society (Vol. 8).
Adams, S., Cody, T., Beling, P. A., Polter, S., & Farinholt, K. (2020). Hierarchical classification for unknown faults. In 2020 ieee international conference on prognostics and health management (icphm) (pp. 1–8).
Adams, S., Meekins, R., Beling, P. A., Farinholt, K., Brown, N., Polter, S., & Dong, Q. (2017). A comparison of feature selection and feature extraction techniques for condition monitoring of a hydraulic actuator. In Annual conference of the phm society (Vol. 9).
Adams, S., Meekins, R., Beling, P. A., Farinholt, K., Brown, N., Polter, S., & Dong, Q. (2019). Hierarchical fault classification for resource constrained systems. Mechanical Systems and Signal Processing, 134, 106266.
Adams, S., Meekins, R., Farinholt, K., Hipwell, N., Desrosiers, M., & Beling, P. A. (2018). One-class support vector machines for structural health monitoring on wave energy converters. In 2018 ieee international conference on prognostics and health management (icphm) (pp. 1–8).
Breiman, L. (2001). Random forests. Machine learning, 45, 5–32.
Cody, T., Adams, S., Beling, P., & Freeman, L. (2022). On valuing the impact of machine learning faults to cyber-physical production systems. In 2022 ieee international conference on omni-layer intelligent systems (coins) (pp. 1–6).
Cody, T., Adams, S., & Beling, P. A. (2017). Unsupervised deep learning for gear health monitoring. In Annual conference of the phm society (Vol. 9).
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Cody, T., Adams, S., & Beling, P. A. (2022). Empirically measuring transfer distance for system design and operation. IEEE Systems Journal, 16(3), 4962–4973.
Cody, T., Adams, S., Beling, P. A., Polter, S., Farinholt, K., Hipwell, N., . . . Meekins, R. (2019). Transferring random samples in actuator systems for binary damage detection. In 2019 ieee international conference on prognostics and health management (icphm) (pp. 1–7).
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Helwig, N., Pignanelli, E., & Sch¨utze, A. (2015). Condition monitoring of a complex hydraulic system using multivariate statistics. In 2015 ieee international instrumentation and measurement technology conference (i2mtc) proceedings (pp. 210–215).
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.
Lessmeier, C., Kimotho, J. K., Zimmer, D., & Sextro, W. (2016). Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification. In Phm society european conference (Vol. 3).
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Meekins, R., Adams, S., Farinholt, K., Polter, S., & Beling, P. A. (2020). ROC with cost Pareto frontier feature selection using search methods. Data-Enabled Discovery and Applications, 4, 1–13.
Neupane, D., & Seok, J. (2020). Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review. Ieee Access, 8, 93155–93178.
Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10), 1345–1359.
Rezaeianjouybari, B., & Shang, Y. (2020). Deep learning for prognostics and health management: State of the art, challenges, and opportunities. Measurement, 163, 107929.
Saxena, A., & Goebel, K. (2008). Turbofan engine degradation simulation data set. NASA ames prognostics data repository, 18.
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management (pp. 1–9).
Surucu, O., Gadsden, S. A., & Yawney, J. (2023). Condition monitoring using machine learning: A review of theory, applications, and recent advances. Expert Systems with Applications, 221, 119738.
Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and health management: A review on data driven approaches. Mathematical Problems in Engineering, 2015(1), 793161.
Wen, Y., Rahman, M. F., Xu, H., & Tseng, T.-L. B. (2022). Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective. Measurement, 187, 110276.
Adams, S., Beling, P. A., Farinholt, K., Brown, N., Polter, S., & Dong, Q. (2016). Condition based monitoring for a hydraulic actuator. In Annual conference of the phm society (Vol. 8).
Adams, S., Cody, T., Beling, P. A., Polter, S., & Farinholt, K. (2020). Hierarchical classification for unknown faults. In 2020 ieee international conference on prognostics and health management (icphm) (pp. 1–8).
Adams, S., Meekins, R., Beling, P. A., Farinholt, K., Brown, N., Polter, S., & Dong, Q. (2017). A comparison of feature selection and feature extraction techniques for condition monitoring of a hydraulic actuator. In Annual conference of the phm society (Vol. 9).
Adams, S., Meekins, R., Beling, P. A., Farinholt, K., Brown, N., Polter, S., & Dong, Q. (2019). Hierarchical fault classification for resource constrained systems. Mechanical Systems and Signal Processing, 134, 106266.
Adams, S., Meekins, R., Farinholt, K., Hipwell, N., Desrosiers, M., & Beling, P. A. (2018). One-class support vector machines for structural health monitoring on wave energy converters. In 2018 ieee international conference on prognostics and health management (icphm) (pp. 1–8).
Breiman, L. (2001). Random forests. Machine learning, 45, 5–32.
Cody, T., Adams, S., Beling, P., & Freeman, L. (2022). On valuing the impact of machine learning faults to cyber-physical production systems. In 2022 ieee international conference on omni-layer intelligent systems (coins) (pp. 1–6).
Cody, T., Adams, S., & Beling, P. A. (2017). Unsupervised deep learning for gear health monitoring. In Annual conference of the phm society (Vol. 9).
Cody, T., Adams, S., & Beling, P. A. (2019). A systems theoretic perspective on transfer learning. In 2019 ieee international systems conference (syscon) (pp. 1–7).
Cody, T., Adams, S., & Beling, P. A. (2022). Empirically measuring transfer distance for system design and operation. IEEE Systems Journal, 16(3), 4962–4973.
Cody, T., Adams, S., Beling, P. A., Polter, S., Farinholt, K., Hipwell, N., . . . Meekins, R. (2019). Transferring random samples in actuator systems for binary damage detection. In 2019 ieee international conference on prognostics and health management (icphm) (pp. 1–7).
Farinholt, K. M., Chaudhry, A., Kim, M., Thompson, E., Hipwell, N., Meekins, R., . . . Polter, S. (2018). Developing health management strategies using power constrained hardware. In Phm society conference (Vol. 10).
Fink, O., Wang, Q., Svensen, M., Dersin, P., Lee, W.-J., & Ducoffe, M. (2020). Potential, challenges and future directions for deep learning in prognostics and health management applications. Engineering Applications of Artificial Intelligence, 92, 103678.
Helwig, N., Pignanelli, E., & Sch¨utze, A. (2015). Condition monitoring of a complex hydraulic system using multivariate statistics. In 2015 ieee international instrumentation and measurement technology conference (i2mtc) proceedings (pp. 210–215).
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735–1780.
Lessmeier, C., Kimotho, J. K., Zimmer, D., & Sextro, W. (2016). Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification. In Phm society european conference (Vol. 3).
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems, 33(12), 6999–7019.
Meekins, R., Adams, S., Beling, P. A., Farinholt, K., Hipwell, N., Chaudhry, A., . . . Dong, Q. (2018). Cost-sensitive classifier selection when there is additional cost information. In International workshop on cost-sensitive learning (pp. 17–30).
Meekins, R., Adams, S., Farinholt, K., Polter, S., & Beling, P. A. (2020). ROC with cost Pareto frontier feature selection using search methods. Data-Enabled Discovery and Applications, 4, 1–13.
Neupane, D., & Seok, J. (2020). Bearing fault detection and diagnosis using case western reserve university dataset with deep learning approaches: A review. Ieee Access, 8, 93155–93178.
Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10), 1345–1359.
Rezaeianjouybari, B., & Shang, Y. (2020). Deep learning for prognostics and health management: State of the art, challenges, and opportunities. Measurement, 163, 107929.
Saxena, A., & Goebel, K. (2008). Turbofan engine degradation simulation data set. NASA ames prognostics data repository, 18.
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In 2008 international conference on prognostics and health management (pp. 1–9).
Surucu, O., Gadsden, S. A., & Yawney, J. (2023). Condition monitoring using machine learning: A review of theory, applications, and recent advances. Expert Systems with Applications, 221, 119738.
Tsui, K. L., Chen, N., Zhou, Q., Hai, Y., & Wang, W. (2015). Prognostics and health management: A review on data driven approaches. Mathematical Problems in Engineering, 2015(1), 793161.
Wen, Y., Rahman, M. F., Xu, H., & Tseng, T.-L. B. (2022). Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective. Measurement, 187, 110276.
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Technical Papers