A Robust Model-Based Strategy for Real-Time Fault Detection and Diagnosis in an Electro-Hydraulic Actuator Using Updated Interactive Multiple Model Smooth Variable Structure Filter

Abstract

Abstract In industries where harsh environments and stringent safety requirements are prevalent, the widespread use of applications has made it essential to focus on fault detection and diagnosis (FDD) in hydraulic actuators. To achieve this, model-based FDD techniques are utilized, which employ estimation tools like observers and filters. However, for many applications, particularly in fluid power systems, observability, and parameter uncertainty pose constraints to extracting information and estimating parameters. To address these issues, an efficient form of interactive multiple model (IMM), called updated IMM (UIMM), is applied to an electro-hydraulic actuator (EHA) to detect and isolate persisting friction and leakage faults. UIMM method progresses through a series of models that correspond to the fault condition's progression instead of considering all models at once (as is done in IMM). This reduces the number of models running simultaneously, providing two significant benefits: enhanced computational efficiency and avoidance of the combinatorial explosion. The smooth variable structure filter with variable boundary layer (SVSF-VBL) is used for state and parameter estimation in conjunction with UIMM. SVSF-VBL is a reliable suboptimal estimation method that performs better than the Kalman filter regarding uncertainties related to the system and modeling. The performance of the UIMM method is validated by the simulation of fault conditions for a typical EHA. A fault tolerable control system (FTCS) has been designed to demonstrate the use of the proposed FDD strategy for fault management in a closed-loop system.