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An Artificial Neural Network Model for Monitoring Real-Time Parameters and Detecting Early Warnings in Induced Draft Fan

[+] Author Affiliations
Di Hu, Gang Chen, Tao Yang, Cheng Zhang, Ziwen Wang

Huazhong University of Science and Technology, Wuhan, China

Qianming Chen, Bing Li

Guangdong Yudean Group Co. Ltd., Dongguan, China

Paper No. MSEC2018-6370, pp. V003T02A010; 6 pages
doi:10.1115/MSEC2018-6370
From:
  • ASME 2018 13th International Manufacturing Science and Engineering Conference
  • Volume 3: Manufacturing Equipment and Systems
  • College Station, Texas, USA, June 18–22, 2018
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5137-1
  • Copyright © 2018 by ASME

abstract

This paper describes a method to monitor real time parameters and detect early warnings in induced draft fan (ID FAN). An artificial neural network (ANN) model based on cross-relationships among operating parameters was established. In particular, this paper adopted the pre-training of Restricted Boltzmann machines (RBM) and analyzed the training errors of model. A new approach was proposed to monitor parameters by predicted value of model and distribution law of training error, and the reasonable range of each parameter was defined to detect the early warnings in real time. Combining the historical operational data of the No. 1 induced draft fan of No. 3 generating unit in Shajiao C Power Plant in China, this work used MATLAB to verify and analyze the proposed method. The numerical examples shown that the proposed method has better detection performance than the fixed upper and lower limits in the safety instrumented system (SIS). Moreover, this work can expand to other machinery that could be used in manufacturing easily.

Copyright © 2018 by ASME

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