A random forest model for boiler drum water level prediction and fault diagnosis based on the characteristic variable stagger method

Xiaoming Han; Simin Hu

doi:10.1117/12.2624093

27 March 2022 A random forest model for boiler drum water level prediction and fault diagnosis based on the characteristic variable stagger method

Xiaoming Han, Simin Hu

Author Affiliations +

Proceedings Volume 12169, Eighth Symposium on Novel Photoelectronic Detection Technology and Applications; 121694G (2022) https://doi.org/10.1117/12.2624093
Event: Eighth Symposium on Novel Photoelectronic Detection Technology and Applications, 2021, Kunming, China

Abstract

In order to predict the trend of water level in the boiler drum in advance and to determine the potential fault types that cause abnormalities of water level, A Random Forest model for Boiler Drum water level prediction and fault diagnosis based on the characteristic variable stagger method is proposed. Based on the dynamic characteristics of the drum water level, the lag time difference in the actual operating conditions is identified, the characteristic variables are found upward to build a new sample set, and a Random Forest regression model is constructed to obtain the predicted drum water level, which is brought into the Random Forest fault diagnosis model to obtain the drum water level abnormal fault type. In order to verify the accuracy of the model, it was compared with Logistic Regression and Support Vector Machine models. The experiments show that the method not only can get the predicted value of Boiler Drum water level in advance, but also can quickly diagnose the potential fault types of water level abnormality, which can help the operational staff to make early prediction and take timely countermeasures for different potential fault categories to ensure the safe and stable operation of the unit.

Citation Download Citation

Xiaoming Han and Simin Hu "A random forest model for boiler drum water level prediction and fault diagnosis based on the characteristic variable stagger method", Proc. SPIE 12169, Eighth Symposium on Novel Photoelectronic Detection Technology and Applications, 121694G (27 March 2022); https://doi.org/10.1117/12.2624093

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Statistical modeling

Performance modeling

Fluctuations and noise

Machine learning

Neural networks

Diagnostics

Show All Keywords

Keywords/Phrases

Search In:

Publication Years