Condition Monitoring of Hydraulic Systems by Classifying Sensor Data Streams

Condition-based maintenance (CBM) of hydraulic systems requires methods for condition monitoring: Sensors installed in a hydraulic system for this purpose generate streams of real-time data that must be analyzed to accurately characterize the health of the system. Prior work has developed an experimental hydraulic system with such an installation and yielded a public dataset of sensor readings with associated values of condition variables that quantify the system's health. This paper presents classification-based methods for inferring these condition variables from the sensor data streams. These methods significantly improve on the classification accuracy reported in prior work on this data. Further, this accuracy is maintained even when the number of sensor-based attributes used as input is substantially reduced.