Reconstruction based fault prognosis for continuous processes
نویسندگان
چکیده
In this paper, a multivariate fault prognosis approach for continuous processes with hidden faults is proposed based on statistical process monitoring methods and multivariate time series prediction. It is assumed that the fault is a slowly time-varying autocorrelated process and can be completely reconstructed. Fault magnitude is estimated first via reconstruction, then predicted by a vector AR model with wavelet based denoising. Given the fault direction, a new index is proposed to detect the fault, which integrates fault detection and prognosis together. Case studies on a continuous stirred tank reactor and the Tennessee Eastman process demonstrate the effectiveness of the proposed approaches. & 2010 Elsevier Ltd. All rights reserved.
منابع مشابه
An Unknown Input Observer for Fault Detection Based on Sliding Mode Observer in Electrical Steering Assist Systems
Steering assist system controls the force transfer behavior of the steering system and improves the steering probability of the vehicle. Moreover, it is an interface between the diver and vehicle. Fault detection in electrical assisted steering systems is a challenging problem due to frequently use of these systems. This paper addresses the fault detection and reconstruction in automotive elect...
متن کاملReconstruction-based fault prognosis for flue gas turbines with independent component analysis
Online detection and prognosis are very important for the safe operation of flue gas turbines. Compared with univariate monitoring of the process, multivariate process monitoring is more effective and can capture abnormal situation in the early stage. This paper proposes a new multivariate fault prognosis framework for the flue gas turbine with a hidden fault process based on independent compon...
متن کاملA Self-Reconstructing Algorithm for Single and Multiple-Sensor Fault Isolation Based on Auto-Associative Neural Networks
Recently different approaches have been developed in the field of sensor fault diagnostics based on Auto-Associative Neural Network (AANN). In this paper we present a novel algorithm called Self reconstructing Auto-Associative Neural Network (S-AANN) which is able to detect and isolate single faulty sensor via reconstruction. We have also extended the algorithm to be applicable in multiple faul...
متن کاملData Mining for Fault Diagnosis in Dynamic Processes: An approach based on SVM
The major inconvenient for the fault detection and isolation (FDI) in technical processes based on analytical redundancy is the requirement of a very accurate model of the system. By contrary, in the methods based on data handling it is not required of a precise model, since they are based on the manipulation of the information by means of the measured data. Thus, in this work a set of statisti...
متن کاملFault Prognosis with Stochastic Modelling on Critical Points of Discrete Processes
The primary role of a machine tool is produce the good quality parts, but a machine tool goes always through a process of degradation and wear which will affect the accuracy and precision of machining and the quality of products. Therefore, monitoring the degradation of machine tool and quantifying its health is very important. The degradation level of a machine can be qualified by an index whi...
متن کامل