Open AccessResearch on fault analysis of pumping station units based on stack auto-encoder
Author(s) -
Linzhang Zhao,
Chenghao Gao
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1303/1/012089
Subject(s) - softmax function , encoder , stack (abstract data type) , computer science , backpropagation , classifier (uml) , pattern recognition (psychology) , fault (geology) , artificial neural network , artificial intelligence , seismology , programming language , geology , operating system
The stack Auto-encoder is applied to the fault analysis of pumping station units, and a fault analysis model based on stack automatic encoder is constructed. The model consists of an input layer, 3 hidden layers and an output layer to realize feature extraction and dimension reduction of the monitoring data of the pumping station unit. The network uses unsupervised layer-by-layer greedy methods to train, then uses backpropagation algorithms to optimize network parameters and uses the softmax classifier for classification. The experiment proves that the average classification accuracy rate of unit failure and different working conditions reaches 79.8890%, which can provide a certain reference for unit failure analysis.