Abstract
Condition monitoring of equipment can be very effective in predicting faults and taking early corrective actions. As hydraulic systems constitute the core of most industrial plants, predictive maintenance of such systems is of vital importance. Due to the availability of huge data collected from industrial plants, machine learning can be used for this purpose. In this work, a hydraulic system condition monitoring (HSCM) is addressed via a public dataset with 17 sensors distributed throughout the system. Using a set of 6 features extracted from sensory data, the random forest classifier was proven, in the literature, to achieve classification rate exceeding 99% for four independent target classes, namely, Cooler, Valve, Pump and Accumulator. In this paper, sensor dependency is examined and experimental results show that a reduced set of important sensors may be sufficient for the addressed classification task. In addition, feature importance as well as implementation issues, i.e. training time and model size on disk, are analyzed. It is found that the training time can be reduced by 25.7% to 36.4% while the size on disk is reduced by 70.3% to 85.5%, using the optimized models, with only important sensors employed, in comparison with the basic model, with full set of sensors, while maintaining classification precision.