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软感应用于压缩机测试时间缩短与时间延迟神经网络

Bernardo B Schwedersky¹, Rodolfo C C Flesch², João P Z Machado²

¹Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Rua Gomes Carneiro 01, Pelotas, 96010-610, RS, Brazil.

ISA transactions

|February 15, 2026

概括

本研究介绍了一种使用时间延迟神经网络 (TDNN) 的软传感器方法,以加快压缩机性能测试. 这种方法大大缩短了评估时间,在工业环境中节省了近50%.

科学领域:

机械工程机械工程
控制系统工程控制系统工程

背景情况:

传统的压缩机性能评估测试是漫长的,通常超过两个小时,以达到稳定状态条件.
优化测试效率对于制造商来说至关重要,以降低运营成本并提高吞吐量.

研究的目的:

开发和验证基于软传感器的方法,以显著缩短压缩机性能评估测试时间.
监测关键性能参数并预测它们的最终值,以便早期稳定状态检测.

主要方法:

开发一种软传感器,利用一个时延神经网络 (TDNN).
在392个压缩机历史评估数据集上训练TDNN模型.
实施软传感方法,实时监测和预测稳定状态条件.

主要成果:

拟议的方法在初步开发过程中大约减少了50%的测试时间.
在5年的工业应用中,9184次性能评估表明,总测试时间有55%的改善.
超过95%的工业测试显示预测误差低于2%.

结论:

基于软传感器的方法有效地加快了压缩机性能评估,从而大大节省了时间.
TDNN方法提高了运营效率,并为关键性能参数提供了可靠的预测.
一致的工业应用验证了该方法在现实世界制造环境中的有效性和稳定性.

关键词:

压缩机性能压缩机性能状态监控状态监控状态监控工业应用工业应用软传感器软传感器时间延迟神经网络

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