Login / Signup
An optimized CNN-BiLSTM network for bearing fault diagnosis under multiple working conditions with limited training samples.
Baoye Song
Yiyan Liu
Jingzhong Fang
Weibo Liu
Maiying Zhong
Xiaohui Liu
Published in:
Neurocomputing (2024)
Keyphrases
</>
fault diagnosis
training samples
monitoring and fault diagnosis
operating conditions
expert systems
fault detection
neural network
fault detection and diagnosis
chemical process
high dimensional
training set
electronic equipment
learning algorithm
test sample
feature space
number of training samples
power transformers
analog circuits
fuzzy logic
supervised learning
bp neural network
rbf neural network
training data
multi sensor information fusion
multiple faults
rotating machinery
image processing
gas turbine
power plant
multiscale
tennessee eastman
feature extraction