Open AccessModeling of Performance Decay Prediction Based on LSTM-SVM Flexible Circuit Board Processing Roll
Author(s) -
Guanze Huang,
Weijie Li,
Yaohua Deng
Publication year - 2021
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 - 10.1088/1742-6596/1828/1/012027
Subject(s) - support vector machine , normalization (sociology) , computer science , artificial intelligence , pattern recognition (psychology) , vibration , signal processing , test set , set (abstract data type) , computer hardware , digital signal processing , physics , quantum mechanics , sociology , anthropology , programming language
The roller is the core component of the flexible material roll-to-roll equipment, and its performance will affect the processing quality, so it is necessary to predict the health of the roller. In order to improve the prediction accuracy and effectively extract the time sequence information hidden in the signal, a LSTM-SVM-based processing roll performance degradation prediction model is proposed. By collecting the bearing vibration sensor data, extracting the characteristics of the vibration signal after normalization, using the extracted features as the input of the prediction model, inputting a part of the samples as the training set into the LSTM-SVM prediction model, and inputting the model in batches for network training, and Adjustment parameters. After the model is trained, use the test set for testing. Compared with support vector machines, the SLTM-SVM model is more effective in predicting the performance degradation of roll-to-roll equipment.