Prediction and diagnosis of faults in hydraulic systems

Abstract

Fault prediction oVers a new perspective to diagnostic engineering systems by covering a wider area of the diagnostic task. This paper presents the fault prediction and diagnosis process of a knowledge-based diagnostic system that is able to predict and diagnose faults in hydraulic systems. Expert systems technology is used cooperatively with dynamic modelling information and on-line sensor information in a suitable environment for the interaction of symbolic and numerical data. This system has been successfully implemented and tested on an actual electrohydraulic system.

A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm

“…The data-driven method includes hidden Markov model (HMM) based method [5][6][7][8], grey theory based method [9], dynamic Bayesian network (DBN) based method [10,11], and Wavelet neural network (WNN) based method [12]. The qualitative knowledge based method that includes expert system based model [13], Petri net based model [14] and so on can be adopted to establish the forecasting model of the hidden behavior.…”

Hidden Behavior Prediction of Complex Systems Based on Hybrid Information

“…The notations that will be used in this paper are listed as follows: t discrete time index x system behavior which can be either observable or hidden D j jth consequent in BRB θ k rule weight of the kth rule of BRB δ k weight of the attribute in BRB β j,k belief degree assessed to D j in the kth rule β D,k remaining belief degrees unassigned to any D j g nonlinear function modeled by BRB Ψ 1 parameter vector composed of rule weights and belief degrees in system equation…”

“…where the parameters θ k , β j,k and β D,k have been defined in (1). ceq(ψ 2 ) = 0 and c(ψ 2 ) ≤ 0 denote the equality and inequality constraints, respectively, that ψ 2 should satisfy.…”

“…If some qualitative knowledge about a system is known, the qualitative knowledge-based method, such as expert system-based method [1] and Petri net-based method [30], can be used to infer the hidden behavior of the system. If the hybrid information is available, a number of theoretical methods have been employed to use the hybrid information for predicting the hidden behavior, such as hidden Markov model (HMM) [2], [8], [12], [15], [16], [32]- [34], [37], dynamic Bayesian network (DBN) [9], [13], [17], [19], [22], domain knowledge and statistical model-based method [10], qualitative knowledge and linear partial least square-based method [11], and fuzzy logic and artificial neural network (FL-ANN)-based method [18], [25].…”

Fault Diagnosis of Electrohydraulic Systems

“…Fault prognosis is concerned with the estimation of potential future failures and the associated implication to the mechanical system, for example [3, 4; 5]. For example, prognosis aims to enhance maintenance planning and scheduling of expected failures [6].…”