Home > Research > Publications & Outputs > A Data-Driven Statistical Approach for Monitori...

Electronic data


    Accepted author manuscript, 553 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License


Text available via DOI:

View graph of relations

A Data-Driven Statistical Approach for Monitoring and Analysis of Large Industrial Processes

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>31/12/2019
Issue number13
Number of pages6
Pages (from-to)2354-2359
Publication StatusPublished
<mark>Original language</mark>English


Monitoring and fault detection of industrial processes is an important area of research in data science, helping effective management of the plant by the remote operator. In this article, a data-driven statistical model of a process is estimated using the principal component analysis (PCA) method and the
associated probability density function. The aim is to use the model to monitor and detect the incurred faults in the industrial plant. The experimental data are collected by finding the suitable subsystems of a Recycle Gas in Ethylene Oxide production process, and a subset of nine variables are extracted for further statistical analysis of the system. The performance of the developed model for monitoring purpose is evaluated by using faulty and close to faulty inputs as the new test data.