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Data Augmentation Applied to Machine Learning-Based Monitoring of a Pulp and Paper Process
- Source :
- Processes, Volume 7, Issue 12
- Publication Year :
- 2019
- Publisher :
- MDPI AG, 2019.
-
Abstract
- Industrial archived process data represent a convenient source of information for data-driven models, such as artificial neural network (ANN), that can be used for safety and efficiency improvement like early or even predictive fault detection and diagnosis (FDD). Nonetheless, most of the data used for model generation are representative of the process nominal states and therefore are not enough for classification problems intended to determine abnormal process conditions. This work proposes the use of techniques to augment the original real data standards, dismissing the need for experiments that could jeopardize process safety. It uses the Monte Carlo technique to artificially increase the number of model inputs coupled to the nearest neighbor search (NNS) by geometric distances to consistently classify the generated patterns in normal or faulty statuses. Finally, a radial basis function neural network is trained with the augmented data. The methodology was validated by a study case in which 3381 pulp and paper industrial data points were expanded to monitor the formation of particles in a recovery boiler. Only 5.8% of the original process data were examples of faulty conditions, but the new expanded and balanced data collection leveraged the classification performance of the neural network, allowing its future use for monitoring purpose.
- Subjects :
- 0209 industrial biotechnology
Computer science
Nearest neighbor search
Bioengineering
02 engineering and technology
Machine learning
computer.software_genre
Fault detection and isolation
Data-driven
pulp and paper industry
020901 industrial engineering & automation
0202 electrical engineering, electronic engineering, information engineering
Chemical Engineering (miscellaneous)
Data collection
Artificial neural network
business.industry
Process Chemistry and Technology
Process (computing)
neural networks
Monte Carlo technique
study case
machine learning
Data point
Process safety
data-driven
FDD
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 22279717
- Volume :
- 7
- Database :
- OpenAIRE
- Journal :
- Processes
- Accession number :
- edsair.doi.dedup.....ae03b492d0896ddfa93edec2dcc4b712