Abstract
In the present work, vibration, acoustic and thermal signals were correlated to the semi-autogenous grinding mill working parameters such as total power and inlet water flow rate, and then these parameters were monitored using vibration, acoustic and thermal analyses. Next, the influential controlling parameters were obtained to monitor the mill conditions via SPSS software and afterward by exploiting neural network method, the modified controlling parameters were acquired for the development of mill performance. It was found that the mill vibration and the bearing temperature increased with motors power. Also, the results revealed that the mill noise reduced with the increase in water flow rate. Finally, an analytical relationship between the controlling parameters and the noise as a measurable parameter was proposed.
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 callMore From: Iranian Journal of Science and Technology, Transactions of Mechanical Engineering
Disclaimer: 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.
