Abstract
Modern intelligent methods for information processing are used to estimate the filling level of the ball mills. This problem has been topical for a long time. Moreover, the increasing number of the used ball mills makes it even more important. The main objective is that the ball mill's functioning mode, which is optimal from the point of view of the energy efficiency, can be reached, if the mill is loaded with the ore as much as possible. In its turn, such a mode may cause an overloading in case of any additional ore supply. This leads to the balls' and ore grain's coarse blowout and, as a result, the mill emergency stopping. As a consequence, the mill downtime results in economic losses. The main aim of this research is to develop a method for processing of the vibration acceleration signal from the ball mill's pin in order to discover the hidden dependencies in it. Such dependencies will allow us to estimate the mills' filling level more effectively, but they cannot be discovered by the classical methods, like spectrum analysis of the signal amplitude. A pilot ball mill is used in the laboratory conditions in order to reach such results. A vibration acceleration sensor is set at its pin. A ball load is changed during experiments. A training set for a neural network is formed as a result of the spectrum analysis of the signal obtained from the sensor. The neural network helped to find a relation between the vibration acceleration spectrum and the ball mill filling level. After the conducted experiments a conclusion can be made that such a network is insensitive to the noise caused by a change of the ball load. This insensitivity is higher in comparison with the methods, which are used as a basis for the conventional vibration-acoustic analyzers.
Highlights
Постановка задачи исследованияНа сеãоäняøний äенü на ìноãих ãорно-обоãатитеëüных коìбинатах работаþт øаровые ìеëüниöы, в которых изìеëü÷ается руäная ìасса
Обсуждается разработка и исследование метода обработки информации, позволяющего выделять в спектре снимаемого сигнала виброускорения цапфы шаровой мельницы скрытые зависимости и закономерности, позволяющие эффективно оценивать уровень загрузки ее барабана, которые сложно выявить и проанализировать классическими методами, например амплитудным анализом спектра
The main aim of this research is to develop a method for processing of the vibration acceleration signal from the ball mill’s pin in order to discover the hidden dependencies in it. Such dependencies will allow us to estimate the mills’ filling level more effectively, but they cannot be discovered by the classical methods, like spectrum analysis of the signal amplitude
Summary
На сеãоäняøний äенü на ìноãих ãорно-обоãатитеëüных коìбинатах работаþт øаровые ìеëüниöы, в которых изìеëü÷ается руäная ìасса. В работах [5, 6] также рассìатривается спектр сиãнаëа виброускорения, äëя анаëиза котороãо преäëаãается испоëüзоватü нейронные сети пряìоãо распространения. Дëя заäанноãо äиапазона ÷астот äëя кажäоãо уровня заãрузки ìеëüниöы äëя опытов с øуìоì и без øуìа быëи построены ãрафики изìенения критерия J в зависиìости от заãрузки барабана ìеëüниöы, преäставëенные на рис. По резуëüтатаì опытов показано, ÷то ìетоäика обработки инфорìаöии, базируþщаяся суãубо на аìпëитуäноì анаëизе спектра сиãнаëа виброускорения, сиëüно поäвержена разëи÷ныì возìущенияì. В спектре сиãнаëа виброускорения öапфы ìеëüниöы присутствует инфорìаöия об уровне заãрузки барабана, но в усëовиях возìущений она соäержится в боëее сëожных зависиìостях ìежäу отäеëüныìи ãарìони÷ескиìи составëяþщиìи спектра, ÷еì это ìожет выявитü аìпëитуäный анаëиз. Äостато÷но öеëесообразныì выãëяäит приìенение аппарата нейронных сетей äëя обработки спектра сиãнаëа виброускорения öапфы ìеëüниöы в öеëях установëения зависиìости ìежäу уровнеì вибраöии и степенüþ запоëнения ее барабана
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
