Mats Nikus

Causal model based fault diagnosis applied on a paper machine simulator

Abstract The aim of the work presented in this paper is to evaluate the ability of the causal digraph method to detect and isolate faults on a simulated paper machine process. A causal digraph model for the short circulation process of the paper machine was constructed, identified and used to detect and isolate artificial faults in the simulation environment. The fault of headbox slice opening was studied and diagnosed.

Novel Causal Digraph Reasoning For Fault Diagnosis with Application on the Paper Machine Short Circulation Process

Abstract This paper presents an enhanced dynamic causal digraph reasoning method for fault diagnosis and its application to the short circulation process of a paper machine. In order to improve the isolation capability of the original method, an inference mechanism between the arcs of the graph is proposed to locate process faults on the arcs. Application of the proposed method to the paper machine short circulation process is presented at the end of the paper. The results show that the proposed method is able to identify the responsible arcs when the system is affected by a process fault.

Support vector machines for detection of analyzer faults- a case study

Abstract The aim of the work presented in this paper is to assess the ability of support vector machines (SVM) for detecting measurement faults. Two different support vector machine approaches for detecting faults are tested and compared to neural networks. The first method is based on a SVM regression model together with an analysis of the residuals whereas the second method is based on a SVM classifier. The methods were applied to a rigorous first principles based dynamic simulator of a dearomatization process.