自编码
过度拟合
模式识别(心理学)
人工智能
计算机科学
特征(语言学)
断层(地质)
深度学习
特征提取
无监督学习
解码方法
特征学习
机器学习
人工神经网络
算法
地质学
哲学
语言学
地震学
作者
Feng Yu,Jianchang Liu,Dongming Liu,Honghai Wang
标识
DOI:10.1016/j.jtice.2021.104200
摘要
Convolutional autoencoder (CAE) is an unsupervised feature learning method and shows excellent performance in multivariate fault diagnosis. However, CAE cannot guarantee that the extracted feature is always related to the fault type due to its unsupervised self-reconstruction in the pretraining phase. To solve this problem, a new feature learning method, supervised convolutional autoencoder (SCAE) is proposed to pretrain the network and learn representative feature containing internal spatial information and fault information. In the SCAE, process sample and corresponding label are reconstructed by multilayer encoding-decoding the raw sample. Meanwhile, to prevent label information overfitting the network, a minimum difference transformation function is introduced into the loss function. The obtained fault-relevant features can be obviously distinguished between different fault types. The trained pretraining network provides more appropriate predefined parameters for fine-tuning to improve the classification performance. The effectiveness of the proposed method is evaluated by the continuous stirred tank reactor (CSTR) process and the Tennessee Eastman (TE) process.
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