University Of Tasmania
Browse

File(s) under permanent embargo

A probabilistic multivariate method for fault diagnosis of industrial processes

journal contribution
posted on 2023-05-18, 13:08 authored by Yu, H, Khan, F, Vikrambhai GaraniyaVikrambhai Garaniya
A probabilistic multivariate fault diagnosis technique is proposed for industrial processes. The joint probability density function containing essential features of normal operation is constructed considering dependency among the process variables. The dependence structures are modelled using Gaussian copula. The Gaussian copula uses rank correlation coefficients to capture the nonlinear relationships between process variables. For realtime monitoring, the probability of each online data samples is computed under the joint probability density function. Those samples having probabilities violating a predetermined control limit are classified to be faulty. For fault diagnosis, the reference dependence structures ofthe process variables are first determined from normal process data. These reference structures are then compared with those obtained from the faulty data samples. This assists in identifying the root-cause variable(s). The proposed technique is tested on two case studies: a nonlinear numerical example and an industrial case. The performance of the proposed technique is observed to be superior to the conventional statistical methods, such as PCA and MICA.

History

Publication title

Chemical Engineering Research and Design

Volume

104

Pagination

306-318

ISSN

0263-8762

Department/School

Australian Maritime College

Publisher

Inst Chemical Engineers

Place of publication

165-189 Railway Terrace, Davis Bldg, Rugby, England, Cv21 3Br

Rights statement

Copyright 2015 The Institution of Chemical Engineers

Repository Status

  • Restricted

Socio-economic Objectives

Industrial instruments

Usage metrics

    University Of Tasmania

    Exports