MOLA: Enhancing Industrial Process Monitoring Using a Multi-Block Orthogonal Long Short-Term Memory Autoencoder

In this work, we introduce MOLA, a multi-block orthogonal long short-term memory autoencoder paradigm, to conduct accurate, reliable fault detection of industrial processes. To achieve this, MOLA effectively extracts dynamic orthogonal features by introducing an orthogonality-based loss function to...

Full description

Saved in:
Bibliographic Details
Published in:Processes 2024-12, Vol.12 (12), p.2824
Main Authors: Ma, Fangyuan, Ji, Cheng, Wang, Jingde, Sun, Wei, Tang, Xun, Jiang, Zheyu
Format: Article
Language:English
Subjects:
Accuracy
Bayesian analysis
Data processing
Data transmission
Deep learning
Distributed control systems
Fault detection
Long short-term memory
Monitoring
Multivariate analysis
Neural networks
Orthogonality
Process variables
Redundancy
Statistical methods
Statistics
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this work, we introduce MOLA, a multi-block orthogonal long short-term memory autoencoder paradigm, to conduct accurate, reliable fault detection of industrial processes. To achieve this, MOLA effectively extracts dynamic orthogonal features by introducing an orthogonality-based loss function to constrain the latent space output. This helps eliminate the redundancy in the features identified, thereby improving the overall monitoring performance. On top of this, a multi-block monitoring structure is proposed, which categorizes the process variables into multiple blocks by leveraging expert process knowledge about their associations with the overall process. Each block is associated with its specific orthogonal long short-term memory autoencoder model, whose extracted dynamic orthogonal features are monitored by distance-based Hotelling’s T2 statistics and quantile-based cumulative sum (CUSUM) designed for multivariate data streams that are nonparametric and heterogeneous. Compared to having a single model accounting for all process variables, such a multi-block structure significantly improves overall process monitoring performance, especially for large-scale industrial processes. Finally, we propose an adaptive weight-based Bayesian fusion (W-BF) framework to aggregate all block-wise monitoring statistics into a global statistic that we monitor for faults. Fault detection speed and accuracy are improved by assigning and adjusting weights to blocks based on the sequential order in which alarms are raised. We demonstrate the efficiency and effectiveness of our MOLA framework by applying it to the Tennessee Eastman process and comparing the performance with various benchmark methods.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr12122824