A two-step hypergraph reduction based fitting method for unbalanced data

Xiao, Guobao; Zhou, Xiong; Yan, Yan; Wang, Hanzi

doi:10.1016/j.patrec.2018.11.003

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…The data-level approach consists of re-sampling methods, which mainly include increasing the number of minority examples by replicating observations in the original dataset-over-sampling-or decreasing the number of majority examples by removing some of them-under-sampling [19][20][21][22]. Duplications and uncertainties are introduced by re-sampling methods that might lead to overfitting or loss of information [15].…”

Section: Introductionmentioning

confidence: 99%

Data Augmentation Applied to Machine Learning-Based Monitoring of a Pulp and Paper Process

et al. 2019

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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.

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