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A neural networks approach of process hult diagnosis using time series collected data through oil condition monitoring

机译:通过油分监测使用时间序列收集数据的过程HULT诊断的神经网络方法

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In this paper was used the data set collected in a research project between private companies from Romania and Italy,for the development of a basic approach of artificial neural network techniques,as an application in Matlab,aiming to detect the degree of degradation of oil,an automated installation,measuring online the physicochemical properties of the oil.Physical-chemical parameters measured lead to the creation of generous time series,but accessible by numerical and statistical calculation,for the application of artificial intelligence techniques.Applying neural network techniques to parameters that measure oil degradation,oxidation and humidity have generated the results of this work.The main function of monitoring the state of operation of a mechanical system,machine,or plant is to provide the almost correct diagnosis of the machine's state and rate of change so that preventive measures can be taken at a given time.
机译:本文使用了从罗马尼亚和意大利私营公司之间的研究项目中收集的数据集,用于开发人工神经网络技术的基本方法,作为Matlab的应用,旨在检测油的降解程度,自动化安装,在线测量油的物理化学性质。测量的物理学参数测量导致慷慨时间序列的创建,但通过数值和统计计算可访问,用于应用人工智能技术。将神经网络技术应用于参数测量油劣化,氧化和湿度产生了这项工作的结果。监测机械系统,机器或工厂的运行状态的主要功能是提供机器状态的几乎正确诊断和变化率可以在给定时间采取预防措施。

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