The estimation of the speed of rotating hydraulic systems (motors) is an important task in processes (control) that merit an exact measurement of the rotating speed. The implementation of a filtering technique based on adaptive filtering algorithms offers a new proposal in the treatment of feedback signals from hydraulic systems (Speed). The implementation of adaptive filtering in obtaining training parameters of Machine Learning algorithms for the estimation of the speed of a variable speed hydraulic system proposes a novel and highly applicable technique. In this article, a speed estimator system for a rotating hydraulic system is proposed using the adaptive filtering technique based on the noise canker topology in conjunction with multilayer neural networks, evaluated by: the mean square error, the absolute average error, the standard deviation and the correlation obtaining values of 0.59, 0.19, 0.23 and 0.99 in comparison with its counterpart of conventional census (transducer). According to the results, the system is appropriate for a speed estimation of the proposed rotary hydraulic system.
|Title of host publication||Advances in Emerging Trends and Technologies - Volume 2|
|Editors||Miguel Botto-Tobar, Joffre León-Acurio, Angela Díaz Cadena, Práxedes Montiel Díaz|
|Number of pages||9|
|State||Published - 1 Jan 2020|
|Event||1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 - quito, Ecuador|
Duration: 29 May 2019 → 31 May 2019
|Name||Advances in Intelligent Systems and Computing|
|Conference||1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019|
|Period||29/05/19 → 31/05/19|
Bibliographical notePublisher Copyright:
© 2020, Springer Nature Switzerland AG.
- Neural networks
- Speed estimator