Hydraulic Actuator Condition Monitoring Dataset for Machine Learning

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.