A Hybrid Missing Data Imputation Method for Batch Process Monitoring Dataset

Batch process monitoring datasets usually contain missing data, which decreases the performance of data-driven modeling for fault identification and optimal control. Many methods have been proposed to impute missing data; however, they do not fulfill the need for data quality, especially in sensor d...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 23; H. 21; S. 8678
Hauptverfasser: Gan, Qihong, Gong, Lang, Hu, Dasha, Jiang, Yuming, Ding, Xuefeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 24.10.2023
Schlagworte:
Algorithms
batch process
Batch processes
Classification
Data processing
data quality
Datasets
Design
Information management
Injection molding
LSTM neural network
Machine learning
Methods
Missing data
missing data imputation
Neural networks
Regression analysis
Time series
Variables
ISSN:1424-8220, 1424-8220
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Zusammenfassung:Batch process monitoring datasets usually contain missing data, which decreases the performance of data-driven modeling for fault identification and optimal control. Many methods have been proposed to impute missing data; however, they do not fulfill the need for data quality, especially in sensor datasets with different types of missing data. We propose a hybrid missing data imputation method for batch process monitoring datasets with multi-type missing data. In this method, the missing data is first classified into five categories based on the continuous missing duration and the number of variables missing simultaneously. Then, different categories of missing data are step-by-step imputed considering their unique characteristics. A combination of three single-dimensional interpolation models is employed to impute transient isolated missing values. An iterative imputation based on a multivariate regression model is designed for imputing long-term missing variables, and a combination model based on single-dimensional interpolation and multivariate regression is proposed for imputing short-term missing variables. The Long Short-Term Memory (LSTM) model is utilized to impute both short-term and long-term missing samples. Finally, a series of experiments for different categories of missing data were conducted based on a real-world batch process monitoring dataset. The results demonstrate that the proposed method achieves higher imputation accuracy than other comparative methods.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s23218678