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Machine Learning for Monitoring of the Solenoid Valves Coil Resistance Based on Optical Fiber Squeezer
参考中译:基于光纤挤压机的电磁阀线圈电阻监测的机器学习


          

刊名：Journal Europeen des Systemes Automatises
作者：Said Amrane（Polydisciplanary Faculty of Taroudant, Ibn Zohr University）
Abdallah Zahidi（National Institute of Posts and Telecommunications）
Mostafa Abouricha（EPTHE, Department of Physics, Faculty of Sciences, Ibn Zohr University）
Nawfel Azami（National Institute of Posts and Telecommunications）
Naoual Nasser（Faculties of Science and Technology, LDEDS）
Mohamed Errai（Polydisciplanary Faculty of Taroudant, Ibn Zohr University）
刊号：737F0064
ISSN：1269-6935
出版年：2021
年卷期：2021, vol.54, no.5
页码：763-767
总页数：5
分类号：TP13
关键词：Machine learning；Monitoring；Solenoid valve；Coil resistance；Fiber squeezer
参考中译：机器学习；监测；电磁阀；线圈电阻；纤维挤压机
语种：eng
文摘：Solenoid valves represent indispensable elements in various engineering systems. Their failure can lead to unexpected problems. This failure may be caused by fluctuations in the coil resistance of the electromagnetic solenoid (EMS) which actuates these solenoid valves. Hence the need to monitor this parameter for a preventive maintenance of these actuators. The proposed method consists to use supervised machine learning to monitor coil resistance of the EMS valve. The EMS valve is coupled to an optical fiber squeezer which, acts as a force sensor. The solenoid armature applies a mechanical force to the optical fiber and changes the polarization state of the light that travels through the optical fiber and then this force infects the power of the light. A Simulink model is used to determine the open loop system step response. The identification of the system allows obtaining its transfer function, which depends on the parameters of the EMS and in particular on its coil resistance. By varying the coil resistance while fixing the other physical parameters of the EMS, we generate a database whose elements are the coefficients of the transfer function of the solenoid open loop and the electrical resistance of its coil. The generated database is used for training several supervised machine learning models whose predictors are the elements of the transfer function; the response is the coil resistance. The Gaussian process for regression allows to predict the variations of the coil resistance with the smallest relative error although it takes a relatively long time for the training compared to the other models used.
参考中译：电磁阀是各种工程系统中不可缺少的元件。他们的失败可能会导致意想不到的问题。这种故障可能是由驱动这些电磁阀的电磁线圈(EMS)的线圈电阻波动引起的。因此，需要监测该参数，以便对这些执行器进行预防性维护。提出的方法包括使用有监督机器学习来监测电磁阀的线圈电阻。EMS阀与光纤挤压器相连，光纤挤压器充当力传感器。螺线管电枢向光纤施加机械力，改变通过光纤的光的偏振状态，然后这种力影响光的功率。利用SIMULINK模型确定开环系统的阶跃响应。系统的识别允许获得其传递函数，该传递函数取决于EMS的参数，特别是其线圈电阻。通过改变线圈电阻，同时固定电磁线圈的其他物理参数，生成了以螺线管开环传递函数系数及其线圈电阻为元素的数据库。生成的数据库用于训练几个有监督的机器学习模型，这些模型的预测器是传递函数的元素；响应是线圈电阻。回归的高斯过程能够以最小的相对误差预测线圈电阻的变化，尽管与使用的其他模型相比，训练所需的时间相对较长。



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