过度拟合
联营
稳健性(进化)
规范化(社会学)
特征提取
计算机科学
卷积神经网络
人工智能
模式识别(心理学)
磨坊
数据挖掘
深度学习
人工神经网络
工程类
化学
社会学
基因
机械工程
生物化学
人类学
作者
Peiming Shi,Yue Yu,Hao Gao,Hua Chen
标识
DOI:10.1016/j.ymssp.2022.108903
摘要
Monitoring and maintaining the health states of the rolling mill is a constant concern of the steel industry. Therefore, in this paper, multi-source sensors are mounted on the rolling mill to collect various data. Meanwhile, for better health states monitoring with multi-source sensing data, a new deep learning (DL) method based on the improved one-dimension Convolutional Neural Network (I1DCNN) and the improved two-dimension Convolutional Neural Network (I2DCNN) is proposed. First, I2DCNN is fed the 2D kurtogram images generated from the vibration signals by fast kurtogram, while I1DCNN is fed the acoustic signals. Meanwhile, Group Normalization (GN) is embedded to improve the robustness. More importantly, Global averaging pooling (GAP) replaces the traditional fully connected layer to improve model spatial feature extraction. Then, the overfitting problem is mitigated by introducing the dropout layer. Finally, the imbalanced and limited datasets are conducted to test and evaluate the proposed method. Experimental results suggest that the proposed method can achieve efficient and accurate health states monitoring with multi-source sensing data, compared to the other states of the art DL methods.
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