ConvLSTM and Self-Attention Aided Canonical Correlation Analysis for Multioutput Soft Sensor Modeling

Zhu, Xiuli; Damarla, Seshu Kumar; Hao, Kuangrong; Huang, Biao; Chen, Hongtian

doi:10.1109/tim.2022.3225004

Cited by 10 publications

(3 citation statements)

References 79 publications

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“…Existing the dynamic nature of industrial processes, it is essential to learn past time-step information and capture the dynamic characteristics of an entire series. Unlike common feed-forward networks, long short-term memory network (LSTM) is a dynamic neural network for soft sensor [21][22][23] with recurrent techniques [24] and memory units [25] to capture temporal dynamic behavior. Bidirectional LSTM network (BiLSTM), designed with a bidirectional information transmission channel and allows a better understanding of the temporal correlations within sequences [26].…”

Section: Introductionmentioning

confidence: 99%

Dual temporal attention mechanism-based convolutional LSTM model for industrial dynamic soft sensor

Cui,

Shi,

Huang

et al. 2024

Meas. Sci. Technol.

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Deep learning is an appropriate methodology for modeling complex industrial data in the field of soft sensors, owing to its powerful feature representation capability. Given the nonlinear and dynamic nature of the process industry, the key challenge for soft sensor technology is to effectively mine dynamic information from long sequences and accurately extract features of relevance to quality. A dual temporal attention mechanism-based convolutional long short-term memory network (DTA-ConvLSTM) under an encoder-decoder framework is proposed as a soft sensor model to acquire quality-relevant dynamic features from serial data. Considering different influences of process variables for prediction at multiple time steps and various locations, ConvLSTM and temporal self-attention mechanism are utilized as the encoder to adaptively fuse spatiotemporal features and capture long-term dynamic properties of process in order to capture the trends of industrial variables. Furthermore, a quality-driven temporal attention mechanism is employed throughout the decoding process to dynamically select relevant features to more accurately track quality changes. The encoder-decoder model meticulously analyses the interactions between process and quality variables by incorporating dual-sequence dynamic information to improve the prediction performance. The validity and superiority of the DTA-ConvLSTM model was validated on two industrial case studies of the debutanizer column and sulfur recovery unit. Compared to the traditional LSTM model, the proposed model demonstrated a substantial improvement with the accuracy R2 up to 97.3% and 94.9% and the root mean square error reducing to 0.122 and 0.022.

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Section: Introductionmentioning

confidence: 99%

Dual temporal attention mechanism-based convolutional LSTM model for industrial dynamic soft sensor

Cui,

Shi,

Huang

et al. 2024

Meas. Sci. Technol.

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“…involves intricate chemical reactions and requires precise control for high-quality output, the application of data-driven methods and predictive modelling techniques becomes essential [2]. Process intelligence plays a vital role in the polyester industry by facilitating the accurate prediction of crucial variables such as reaction rates, temperature profiles, and product quality [3]. This information allows for the understanding of trends, evaluation of system state, and early detection of any abnormalities [4].…”

Section: Introductionmentioning

confidence: 99%

Multi-level deep domain adaptive adversarial model based on tensor-train decomposition for industrial time series forecasting

Yang,

Peng,

Chen

et al. 2023

Meas. Sci. Technol.

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The polyester industry is a complex process industry, building a time series prediction model for new production lines or equipment with new sensors can be challenging due to a lack of historical data. The time-series data collected from sensors cross-production-line often exhibit varying distributions. Current domain adaptation (DA) approaches in data-driven time series forecasting primarily concentrate on adjusting either the features or the models, neglecting the intricacies of industrial time series data. Furthermore, constructing deep neural networks for industrial data necessitates substantial computational resources and runtime due to their large and high-dimensional nature. In order to tackle these obstacles, we propose a novel Multi-level deep domain adaptive adversarial model based on tensor-train decomposition (TT-MDAM). Our model aims to strike a dynamic balance between prediction accuracy and runtime efficiency. By integrating multiple perspectives at the feature, trend, and model levels, we leverage DA to enhance the prediction accuracy of our model in the target domain. Additionally, by analyzing the weight matrix of the neural network, we generate a low-rank model to improve operational efficiency. The application of the proposed TT-MDAM approach to both the three-phase flow facility process (TPFF) dataset and a real-world polyester esterification process dataset reveals promising results, outperforming state-of-the-art methodologies in terms of prediction performance. The results indicate that the approach provides a viable solution for building time series prediction models in industrial processes with new equipment or production lines.

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“…A soft sensor [19,20] is an estimation method based on available physical sensors and process parameters to derive the interested physical variables [21,22]. There are primarily three types of soft sensor models: mechanism-based, knowledge-based, and data-driven methods [23].…”

Section: Introductionmentioning

confidence: 99%

Sensor Fault Diagnosis, Isolation, and Accommodation for Heating, Ventilating, and Air Conditioning Systems Based on Soft Sensor

Nie,

Ren,

et al. 2023

Actuators

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Heating, Ventilating, and Air Conditioning (HVAC) systems often suffer from unscheduled maintenance or abnormal shutdown due to the fault of their interior sensor system. Traditional fault diagnosis methods for HVAC sensor systems primarily focus on sensor fault diagnosis and isolation, lacking fault accommodation. Therefore, to realize effective sensor fault detection, identification, and accommodation (SFDIA), a method for HVAC SFDIA based on the soft sensor is proposed. First, a diagnosis soft sensor with multi-variable input is constructed to estimate the output of the physical sensor being diagnosed. The residual between the estimated value of the diagnosis soft sensor and the measurement of the physical sensor is used as an indicator of the sensor’s condition. If the residual exceeds the fault threshold, the sensor is diagnosed to be faulty. In order to maintain valid sensor output, an accommodation soft sensor is constructed using the historical normal value. The erroneous output of the faulty sensor is substituted by the estimated value from the accommodation soft sensor, thereby realizing sensor fault tolerance control. Experimental results demonstrate that the average false alarm rate for sensor fault diagnosis is 1.57% and the average fault diagnosis rate is 96.51%. The predictive mean absolute error (MAE) and root-mean-square error (RMSE) of the recovered soft sensors are 0.0525 and 0.0738, respectively. Thus, the soft sensors developed in this paper exhibit satisfying ability in HVAC SFDIA.

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ConvLSTM and Self-Attention Aided Canonical Correlation Analysis for Multioutput Soft Sensor Modeling

Cited by 10 publications

References 79 publications

Dual temporal attention mechanism-based convolutional LSTM model for industrial dynamic soft sensor

Dual temporal attention mechanism-based convolutional LSTM model for industrial dynamic soft sensor

Multi-level deep domain adaptive adversarial model based on tensor-train decomposition for industrial time series forecasting

Sensor Fault Diagnosis, Isolation, and Accommodation for Heating, Ventilating, and Air Conditioning Systems Based on Soft Sensor

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