Supervised convolutional autoencoder-based fault-relevant feature learning for fault diagnosis in industrial processes,Journal of the Taiwan Institute of Chemical Engineers

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Supervised convolutional autoencoder-based fault-relevant feature learning for fault diagnosis in industrial processes
Journal of the Taiwan Institute of Chemical Engineers ( IF 5.5 ) Pub Date : 2022-01-18 , DOI: 10.1016/j.jtice.2021.104200
Feng Yu _{1,

2} , Jianchang Liu _{1,

2} , Dongming Liu _{1,

2} , Honghai Wang _{1,

2}

Affiliation

Background

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.

Methods

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.

Findings

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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