Fault Detection and Identification of Furnace Negative Pressure System with CVA and GA-XGBoost

Dan Ling (), Chaosong Li, Yan Wang and Pengye Zhang
Additional contact information
Dan Ling: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
Chaosong Li: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
Yan Wang: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China
Pengye Zhang: School of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450000, China

Energies, 2022, vol. 15, issue 17, 1-19

Abstract: The boiler is an essential energy conversion facility in a thermal power plant. One small malfunction or abnormal event will bring huge economic loss and casualties. Accurate and timely detection of abnormal events in boilers is crucial for the safe and economical operation of complex thermal power plants. Data-driven fault diagnosis methods based on statistical process monitoring technology have prevailed in thermal power plants, whereas the false alarm rates of those methods are relatively high. To work around this, this paper proposes a novel fault detection and identification method for furnace negative pressure system based on canonical variable analysis (CVA) and eXtreme Gradient Boosting improved by genetic algorithms (GA-XGBoost). First, CVA is used to reduce the data redundancy and construct the canonical residuals to measure the prediction ability of the state variables. Then, the fault detection model based on GA-XGBoost is schemed using the constructed canonical residual variables. Specially, GA is introduced to determine the optimal hyperparameters of XGBoost and speed up the convergence. Next, this paper presents a novel fault identification method based on the reconstructed contribution statistics, considering the contribution of state space, residual space and canonical residual space. Besides, the proposed statistics renders different weights to the state vectors, the residual vectors and the canonical residual vectors to improve the sensitivity of faulty variables. Finally, the real industrial data from a boiler furnace negative pressure system of a certain thermal power plant is used to demonstrate the ability of the proposed method. The result demonstrates that this method is accurate and efficient to detect and identify the faults of a true boiler.

Keywords: furnace negative pressure; fault detection; canonical variable residual analysis; XGBoost; reconstructed variable contribution (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/17/6355/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/17/6355/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:17:p:6355-:d:902974

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().