Open AccessAuto-encoder based fault early warning model for primary fan of power plant
Author(s) -
Kuan Lu,
Song Gao,
Wei Sun,
Zhe Jiang,
Xiangrong Meng,
Yikui Zhai,
Yingkun Han,
Mengmeng Sun
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/358/4/042060
Subject(s) - autoencoder , computer science , construct (python library) , invertible matrix , encoder , artificial intelligence , operator (biology) , artificial neural network , state (computer science) , power (physics) , feature (linguistics) , matrix (chemical analysis) , fault (geology) , control engineering , pattern recognition (psychology) , machine learning , control theory (sociology) , engineering , algorithm , mathematics , control (management) , philosophy , materials science , repressor , linguistics , chemistry , composite material , operating system , biochemistry , quantum mechanics , transcription factor , programming language , physics , seismology , pure mathematics , gene , geology
Primary fan system plays an important role in the operation of a power plant. However, due to the complicated working conditions of the primary fan and the strong coupling characteristics of multi-state variables, it is necessary to carry out feature engineering before using multivariate state estimation technique (MSET). In addition, no-linear operator should be designed to make sure matrix being invertible. This paper proposes an Auto-encoder based model to automatically construct a normal state memory matrix through unsupervised learning of neural networks. It reduces human intervention as well as the difficulty in giving a suitable non-linear operator design. This model is applied to the early warning of primary fan failure in a power plant in eastern Shandong Province.