A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network.

By Yalin Wang, Zhuofu Pan, Xiaofeng Yuan, Chunhua Yang, W. Gui. ISA transactions, 2019

363 citations recorded by the source corpus.

Deep learning networks have been recently utilized for fault detection and diagnosis (FDD) due to its effectiveness in handling industrial process data, which are often with high nonlinearities and strong correlations. However, the valuable information in the raw data may be filtered with the layer-wise feature compression in traditional deep networks. This cannot benefit for the subsequent fine-tuning phase of fault classification. To alleviate this problem, an extended deep belief network (EDBN) is proposed to fully exploit useful information in the raw data, in which raw data is combined with the hidden features as inputs to each extended restricted Boltzmann machine (ERBM) during the pre-training phase. Then, a dynamic EDBN-based fault classifier is constructed to take the dynamic characteristics of process data into consideration. Finally, to test the performance of the proposed method, it is applied to the Tennessee Eastman (TE) process for fault classification. By comparing EDBN and DBN under different network structures, the results show that EDBN has better feature extraction and fault classification performance than traditional DBN.

Subfields: Control and Systems Engineering, Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/2f7b31f70dd73522464d73e7d623bc66fab6f970

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In Control and Systems Engineering

Citations in the corpus

GANMcC: A Generative Adversarial Network With Multiclassification Constraints for Infrared and Visible Image Fusion (2021, background): “Some speciﬁc network architectures can learn the distribution of observation data, such as SQAE [25], DBN [26], and LSTM [27], but they often need labels.”
Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development (2020, background): “However, these soft sensors are mainly based on static deep networks, such as deep belief networks (DBN) [28] and stacked autoencoders (SAE) [29], [30], in which data samples are assumed to be independent and identically distributed (the i.i.d. assumption).”