Soft Sensing for Time Series With Irregular Sampling Internals Based on a Denoising Interval Attention LSTM Network

The prediction of key quality variables plays an important role in industrial status identification and monitoring. Due to process disturbance and hard device limitation, data collection in modern industries often exhibits high noise and irregular data sampling. To solve the above problems, this art...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. PP; S. 1 - 13
Hauptverfasser: He, Yuchen, Yang, Xueqin, Qian, Lijuan, Yao, Le, Ye, Lingjian, Wu, Ping, Ye, Gangyue, Ye, Weirong, Shen, Yafang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 21.08.2025
Schlagworte:
Adaptation models
Autoencoders
Data mining
Feature extraction
Interval attention LSTM (IALSTM)
irregular sampling
Long short term memory
Noise
Noise reduction
quality-related information
Real-time systems
soft sensing
Soft sensors
stacked supervised and reconstructed input denoising autoencoder (SSRDAE)
Time series analysis
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:The prediction of key quality variables plays an important role in industrial status identification and monitoring. Due to process disturbance and hard device limitation, data collection in modern industries often exhibits high noise and irregular data sampling. To solve the above problems, this article proposes a stacked supervised and reconstructed input denoising autoencoder integrated with internal attention long short-term memory (SSRDAE-IALSTM) network for soft sensing modeling. First, a stacked supervised and reconstructed input denoising autoencoder (SSRDAE) is designed. Compared with the original DAE, each supervised and reconstructed input DAE (SRDAE) can simultaneously reconstruct the process data and quality data at the output layer, aiming to reduce information loss and extract quality-related features. Second, the denoised features are fed into the interval attention LSTM (IALSTM) to adjust the influence of different historical samples on the current sample in irregular sampling data to capture long-term temporal features. Finally, performance validations are carried out on an industrial debutanizer column and a penicillin fermentation process. The experimental results show that the proposed model can enhance the learning ability of process features and obtain better prediction performance than other comparison methods.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2025.3598583