Equipment Fault Prediction in Smart Factory Based on Improved Long and Short Memory Network
Combined with real-time extracted factory data,fault prediction of equipment is an important step in smart factory construction.Introduce the intelligent factory equipment fault prediction method based on the improved long and short memory network.The collected equipment operation data are normalized and outlier treated to ensure uniform range of data and eliminate interference of outliers.Principal component analysis(PCA)was used to reduce the preprocessed da-ta,and features with high information gain were extracted and selected from the reduced data based on timing analysis.Design an improved long-short memory network model for smart factory equipment fault prediction.The model outputs the fault prediction results through the fully connected layer.
NETL
NSTL
万方数据
