Detection of Faults in Electro-Hydrostatic Actuators Using Feature Extraction Methods and an Artificial Neural Network

Electro-hydrostatic actuators (EHAs) are a type of hydraulic actuators which use pumps rather than valves to control the motion. As a result, they are more efficient than the valve-operated actuators. This paper presents an AI-based internal leakage detection algorithm for a single-rod EHA. Actuator internal leakage has been chosen to demonstrate the efficacy of the algorithm. Based on the sensitivity of various measures to varying levels of internal leakage, indicators are derived from the easy to obtain pressure measurements and a fault decision algorithm for quantifying the level of internal leakage in the actuator is established. This paper presents a new architecture of an artificial neural network (ANN) for detecting the existence of an internal leakage fault as labelled data. First, a sensitivity analysis is used to select a measure candidate for further research. Second, the measure chosen is analyzed using feature extraction methods. This step aims to extract hidden features to maximize the internal leakage fault detection. Finally, the fault detection algorithm classification efficiency is assessed by studying the detection rate of the proposed architecture. The experimental results show that the developed algorithm can detect internal leakage faults with 99.46% accuracy.