Soft-sensing for compressor test time reduction with time-delay neural networks.
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| Title: | Soft-sensing for compressor test time reduction with time-delay neural networks. |
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| Authors: | Schwedersky, Bernardo B.1 (AUTHOR) bernardo@inf.ufpel.edu.br, Flesch, Rodolfo C.C.1,2 (AUTHOR) rodolfo.flesch@ufsc.br, Machado, João P.Z.2 (AUTHOR) joao.zomer.m@posgrad.ufsc.br, Nascimento, Ahryman S.B.2 (AUTHOR) a.nascimento@labmetro.ufsc.br, Schaefer, Maurício M.3 (AUTHOR) mauricio.m.schaefer@nidec-ga.com, Moser, Diogo R.3 (AUTHOR) diogo.r.moser@nidec-ga.com |
| Source: | ISA Transactions. Jul2026, Vol. 174, p287-297. 11p. |
| Subjects: | Artificial neural networks, Compressor performance, Machine learning, Prediction models, Industrial applications |
| Abstract: | This study proposes a soft-sensor-based method to significantly shorten compressor performance evaluation tests and presents the results of its industrial application over five years by a compressor manufacturer. Traditional approaches demand long testing times to reach steady-state conditions, with overall test durations frequently surpassing two hours. In this work, a soft sensor based on a time-delay neural network (TDNN) was developed to monitor steady-state conditions of key performance parameters – cooling capacity, power consumption, and coefficient of performance – and to predict their final values. A dataset of 392 compressor evaluations was used for model development, and the proposed method achieved an overall reduction in test duration of close to 50%. This is accomplished because the proposed method incorporates a soft-sensing approach, trained on historical data, facilitating early detection of steady-state conditions and accelerating testing procedures. During five years of real industrial application, with the proposed approach tested in 9184 performance evaluations, this method demonstrated a 55% improvement in total test time, with more than 95% of the tests showing prediction errors below 2%. Therefore, the proposed tools resulted in consistent time savings and increased operational efficiency during their industrial evaluation. • A soft-sensing framework using time-delay neural networks is proposed. • It monitors steady-state conditions and predicts performance variables of compressors. • A 55% reduction in compressor testing time was achieved in industrial applications. • Prediction errors below 2% were observed in 95% of the tests. • Validated over five years on 9184 real-world compressor evaluations. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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