Proposal of a Fast Model-Based Prognostic Paradigm for Electrohydraulic Actuators affected by Multiple Failures

The electrohydraulic actuator (EHA), and in particular the servovalves (SVs) regulating their hydraulic power are complex devices and can fail in several ways. Developing a fault detection algorithm able to identify the precursors of the above mentioned EHA failure and its degradation pattern is thus beneficial for anticipating the incoming failure and alerting the maintenance crew so as to properly schedule the servomechanism replacement. To this purpose, authors propose a new model-based fault detection and identification (FDI) technique able to perform an early detection of two of the most common types of SV progressive failures (dry friction acting on servovalve spool and contamination of the first stage filter).