Fault Diagnosis of a Hydraulic Power System Using an Artificial Neural Network

Abstract

This paper deals with the problem of fault detection, isolation and identification of a hydraulic power system. A proposed fault diagnostic scheme (FDS) using an artificial neural network (ANN) is investigated. A feedforward neural network is employed to diagnose two commonly occurring faults of the hydraulic power system: actuator internal leakage and valve spool blockage. The characterizing model of each fault is derived. The fault diagnostic scheme is applied to a hydraulic power test rig to diagnose real encountered faults. The ANN based FDS has been trained with sufficient data of the faults. Extensive experiments have been carried out and their results are presented and discussed. The experimental results have showed that the trained network has the capability to detect and identify various severity magnitudes of the faults of interest. Furthermore, the trained ANN based FDS has the ability to identify fault levels of untrained fault cases accurately. Therefore, the validity of the proposed FDS as a diagnostic tool for the hydraulic actuator internal leakage and the valve blockage has been assured. Finally, the proposed fault diagnostic scheme can be practically implemented.

A design of an intelligent maintenance integrated system into manufacturing systems

“…[16] It is clear from the mathematical models of the oil leakage and the valve spoolblockage faults, that the variables: x p , P 1 , and P 2 and their derivatives are sufficient to describe the two faults. Hence, only three sensors are sufficient to diagnose the considered faults.…”

Robust Fault-Tolerant Control of an Electro-Hydraulic Actuator With a Novel Nonlinear Unknown Input Observer

“…In another study, the actuator internal leakage and valve spool blockage are diagnosed by the NN without the presence of disturbances or sensor faults. Because the linearized model is used, it reduces the inherent nonlinearity of the system [42]. In [43], the position controller is tolerant with actuator internal leakage and robust with parametric uncertainties in the EHA in which the sensor fault is not considered.…”

P1 - Intelligentes Condition Monitoring von hydraulischen Anlagen

“…Am Markt etablierte CM-Systeme beurteilen den Anlagenzustand meist in der Zeit-oder Frequenzdomäne anhand einer Schwellwertuntersuchung der einzelnen Sensordatenströme. Daneben finden sich in der Literatur verschiedene multivariate Ansätze zur Zustandsüberwachung von hydraulischen Anlagen, die auf künstlichen neuronalen Netzen [3], Entscheidungsbäumen [4] und semantisch-statistischer Analyse [5] basieren. In der vorliegenden Arbeit soll ein statistisches Verfahren gezeigt werden, das für eine automatisierte Auswertung geeignet und mit geringem Aufwand auf geänderte Rahmenbedingungen (Ausfall eines Sensors, Auftreten eines neuen Schadensfalls, Änderung der Anlagenkonfiguration) adaptierbar ist.…”