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4 results on '"forecasting failures"'

1. Improved GRU prediction of paper pulp press variables using different pre-processing methods.

Author

Mateus, Balduíno César, Mendes, Mateus, Torres Farinha, José, Marques Cardoso, António, Assis, Rui, and Soltanali, Hamzeh

Subjects
PAPER pulp, RECURRENT neural networks
Abstract

Predictive maintenance strategies are becoming increasingly more important with the increased needs for automation and digitalization within pulp and paper manufacturing sector. Hence, this study contributes to examine the most efficient pre-processing approaches for predicting sensory data trends based on Gated Recurrent Unit (GRU) neural networks. To validate the model, the data from two paper pulp presses with several pre-processing methods are utilized for predicting the units' conditions. The results of validation criteria show that pre-processing data using a LOWESS in combination with the Elimination of discrepant data filter achieves more stable results, the prediction error decreases, and the predicted values are easier to interpret. The model can anticipate future values with MAPE, RMSE and MAE of 1.2, 0.27 and 0.30 respectively. The errors are below the significance level. Moreover, it is identified that the best hyperparameters found for each paper pulp press must be different. [ABSTRACT FROM AUTHOR]

Published
2023
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2. Improved GRU prediction of paper pulp press variables using different pre-processing methods

Author

Balduíno César Mateus, Mateus Mendes, José Torres Farinha, António Marques Cardoso, Rui Assis, and Hamzeh Soltanali

Subjects
Deep learning, LOWESS, forecasting failures, industrial press, recurrent neural network, predictive maintenance, Technology, Manufactures, TS1-2301, Business, HF5001-6182
Abstract

ABSTRACTPredictive maintenance strategies are becoming increasingly more important with the increased needs for automation and digitalization within pulp and paper manufacturing sector.Hence, this study contributes to examine the most efficient pre-processing approaches for predicting sensory data trends based on Gated Recurrent Unit (GRU) neural networks. To validate the model, the data from two paper pulp presses with several pre-processing methods are utilized for predicting the units’ conditions. The results of validation criteria show that pre-processing data using a LOWESS in combination with the Elimination of discrepant data filter achieves more stable results, the prediction error decreases, and the predicted values are easier to interpret. The model can anticipate future values with MAPE, RMSE and MAE of 1.2, 0.27 and 0.30 respectively. The errors are below the significance level. Moreover, it is identified that the best hyperparameters found for each paper pulp press must be different.

Published
2023
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3. Классификационный подход на основе комбинации глубоких нейронных сетей для прогнозирования отказов сложных многообъектных систем

Subjects
прогнозирование отказов, Forecasting failures, data-driven methods, методы на основе данных данных, deep neural networks, глубокие нейронные сети, LSTM, CNN
Abstract

Научно-технический прогресс способствовал бурному росту сложности систем и выполняемых ими функций, что особенно характерно для различных отраслей современной примышленности. Здесь цена отказа или сбоя оборудования может быть очень высокой а иногда и привести к неоценимым потерям, связанным с гибелью людей. Обслуживание подобных систем требует высоких материальных затрат, и всё же не исключает возможного возникновения сбоев. Это свидетельствует о том, что задача обеспечения надежности сложных многообъектных систем еще далека от своего решения. В связи с этим в настоящее время на первое место выходит задача обеспечения надежного функционирования систем при минимизации затрат на их содержание и техническое обслуживание. Решение которой невозможно без разработки и внедрения интеллектуальных систем, выполняющих функции предсказательной аналитики и предсказательного технического обслуживания. В данной статье предлагается гибридная нейросетевая модель прогнозирования отказов сложных многообъектных систем на основе классификационного подхода, направленная на повышение эксплуатационной надежности оборудования при минимальных затратах. Представлены результаты вычислительных экспериментов, подтверждающие высокую эффективность предложенного решения., Scientific and technical progress has contributed to a rapid increase in the complexity of systems and their functions, which is especially characteristic of various fields of modern industry. Here, the cost of failure of equipment can be very high and sometimes lead to invaluable losses associated with the loss of life. Maintenance of such systems requires high material costs, but still does not exclude the possibility of failures. This indicates that the problem of ensuring the reliability of complex multi-object systems is still far from being solved. In this regard, the task of ensuring reliable operation of systems while minimizing the cost of their maintenance and maintenance is now in the first place. The solution of this problem is impossible without the development and implementation of intelligent systems that perform the functions of predictive analytics and predictive maintenance. This article proposes a hybrid neural network model for predicting failures of complex multi-object systems based on the classification approach, aimed at improving the operational reliability of equipment at minimal cost. The results of computational experiments confirming the high efficiency of the proposed solution are presented, МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ, Выпуск 2 (29) 2020

Published
2020
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4. Deteção e previsão de falhas em equipamentos de produção industrial

Author

Silva, Daniel Filipe Ferreira da and Ramos, Patrícia Alexandra Gregório

Subjects
Modelos ARIMA, Forecasting failures, Previsão de falhas, ARIMA models, Manutenção preditiva, Manufacturing equipment, Predictive maintenance, Equipamento industrial
Abstract

Submitted by Iolanda Valente (ivalente@iscap.ipp.pt) on 2015-07-21T12:55:40Z No. of bitstreams: 1 Daniel_Silva_ML_2013.pdf: 1127777 bytes, checksum: e317de5deed15256820f01ca2532fdfb (MD5) Made available in DSpace on 2015-07-21T12:55:49Z (GMT). No. of bitstreams: 1 Daniel_Silva_ML_2013.pdf: 1127777 bytes, checksum: e317de5deed15256820f01ca2532fdfb (MD5) Previous issue date: 2013

Published
2013

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