Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development

Yuan, Xiaofeng; Li, Lin; Shardt, Yuri A.W.; Wang, Yalin; Yang, Chunhua

doi:10.1109/tie.2020.2984443

Cited by 316 publications

(85 citation statements)

References 41 publications

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“…On the other hand, the hierarchical structure of the deep learning network can enhance its learning efficiency on link quality data. There are many different deep learning networks, like dynamic convolution neural network (DCNN) [15], spatiotemporal attention-based long-short-term-memory (STA-LSTM) [16] and layer-wise data augmentation-based stacked autoencoder (LWDA-SAE) [17]. With the characteristics of layer-by-layer processing, deep learning is able to extract features of higher levels, which is of benefit to feature learning of link quality.…”

Section: Related Workmentioning

confidence: 99%

A Link Quality Estimation Method for Wireless Sensor Networks Based on Deep Forest

Shu

2021

IEEE Access

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Section: Related Workmentioning

confidence: 99%

A Link Quality Estimation Method for Wireless Sensor Networks Based on Deep Forest

Shu

2021

IEEE Access

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“…SS implementation often requires the use of black-box nonlinear dynamical identification strategies, which uses data collected from the distributed control system [ 11 ] and stored in the historical database. To achieve this aim, machine learning (ML) techniques are mostly used, ranging from Support Vector Regression [ 12 ], Partial Least Square [ 13 ], and classical multilayer perceptrons [ 1 , 14 , 15 , 16 , 17 ] to more recent deep architectures, such as deep belief networks [ 9 , 18 , 19 , 20 ], long short-term memory networks (LSTMs) [ 21 , 22 ], and stacked autoencoders [ 23 , 24 , 25 , 26 ]. Bayesian approaches [ 27 ], Gaussian Processes Regression [ 28 ], Extreme Learning Machines [ 29 ], and adaptive methods, [ 30 , 31 , 32 ] are also used.…”

Section: Introductionmentioning

confidence: 99%

RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process

Curreri

Patané

Xibilia

2021

Sensors

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The design and application of Soft Sensors (SSs) in the process industry is a growing research field, which needs to mediate problems of model accuracy with data availability and computational complexity. Black-box machine learning (ML) methods are often used as an efficient tool to implement SSs. Many efforts are, however, required to properly select input variables, model class, model order and the needed hyperparameters. The aim of this work was to investigate the possibility to transfer the knowledge acquired in the design of a SS for a given process to a similar one. This has been approached as a transfer learning problem from a source to a target domain. The implementation of a transfer learning procedure allows to considerably reduce the computational time dedicated to the SS design procedure, leaving out many of the required phases. Two transfer learning methods have been proposed, evaluating their suitability to design SSs based on nonlinear dynamical models. Recurrent neural structures have been used to implement the SSs. In detail, recurrent neural networks and long short-term memory architectures have been compared in regard to their transferability. An industrial case of study has been considered, to evaluate the performance of the proposed procedures and the best compromise between SS performance and computational effort in transferring the model. The problem of labeled data scarcity in the target domain has been also discussed. The obtained results demonstrate the suitability of the proposed transfer learning methods in the design of nonlinear dynamical models for industrial systems.

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“…In [14], an intelligent fault diagnosis was proposed based on a deep echo state network (ESN) and a hybrid evolutionary algorithm. Recently, deep learning models have been combined with different approaches such as SAE [15], deep belief network (DBN) [16], and LSTM [17], to improve the performance of fault detection and data modeling.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator

2021

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The electrical generator is the key part of the electrical generation system for the oil and gas industry, and it is easy to fail, which disturbs the availability and reliability of the electrical generation in the power industry. Therefore, extracting and diagnosing the fault features from the process signals are useful to diagnose the status of the machine. Though, a common challenge in many applied applications is the practical knowledge about the risk of failure or historical records, which is totally unlabeled and difficult to be identified by traditional fault approaches. Hence, in the present study, a novel deep learning (DL) framework is proposed to fill the gap by balancing the three stages of fault feature extraction, fault detection, and parameter optimization based on the long short term memory-recurrent neural networks (RNN-LSTM), stacked autoencoders (SAE), and particle swarm optimization (PSO) techniques. The suggested framework focuses on failure detection through a sequence of numerous features for the unlabeled historical data and unknown anomaly. To validate the effectiveness of the proposed DL framework, an experiment for failure detection of the electrical generator was conducted for the data of risky environment at Yemen oil and gas plant. The experimental results compared with the earlier studies validate that, the DL framework can address the faults for vibration signals of the electrical generator in a well-diagnosis performance effectively. INDEX TERMSDeep learning (DL), Fault detection, Long short-term memory (LSTM), Oil and gas plant, Recurrent neural networks (RNN), Stacked autoencoders (SAE) This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development

Cited by 316 publications

References 41 publications

A Link Quality Estimation Method for Wireless Sensor Networks Based on Deep Forest

A Link Quality Estimation Method for Wireless Sensor Networks Based on Deep Forest

RNN- and LSTM-Based Soft Sensors Transferability for an Industrial Process

A Novel Deep Learning Framework Based RNN-SAE for Fault Detection of Electrical Gas Generator

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