%0 Journal Article %1 IJACSA.2013.040509 %A Farzaneh Ahmadzadeh, Jan.Lundberg %D 2013 %J International Journal of Advanced Computer Science and Applications(IJACSA) %K ; Analysis; Artificial Component Condition Maintenance Monitoring. Principal Remaining Wear life; network; neural prediction; scheduling useful %N 5 %T Application of multi regressive linear model and neural network for wear prediction of grinding mill liners %U http://ijacsa.thesai.org/ %V 4 %X The liner of an ore grinding mill is a critical component in the grinding process, necessary for both high metal recovery and shell protection. From an economic point of view, it is important to keep mill liners in operation as long as possible, minimising the downtime for maintenance or repair. Therefore, predicting their wear is crucial. This paper tests different methods of predicting wear in the context of remaining height and remaining life of the liners. The key concern is to make decisions on replacement and maintenance without stopping the mill for extra inspection as this leads to financial savings. The paper applies linear multiple regression and artificial neural networks (ANN) techniques to determine the most suitable methodology for predicting wear. The advantages of the ANN model over the traditional approach of multiple regression analysis include its high accuracy.

@article{IJACSA.2013.040509, abstract = {The liner of an ore grinding mill is a critical component in the grinding process, necessary for both high metal recovery and shell protection. From an economic point of view, it is important to keep mill liners in operation as long as possible, minimising the downtime for maintenance or repair. Therefore, predicting their wear is crucial. This paper tests different methods of predicting wear in the context of remaining height and remaining life of the liners. The key concern is to make decisions on replacement and maintenance without stopping the mill for extra inspection as this leads to financial savings. The paper applies linear multiple regression and artificial neural networks (ANN) techniques to determine the most suitable methodology for predicting wear. The advantages of the ANN model over the traditional approach of multiple regression analysis include its high accuracy.}, added-at = {2014-02-21T08:00:08.000+0100}, author = {{Farzaneh Ahmadzadeh}, Jan.Lundberg}, biburl = {https://www.bibsonomy.org/bibtex/2ca5d56881f8b30922f991466f807ec31/thesaiorg}, interhash = {4af6ec2c55cc02c5a5e9b0cdad51e112}, intrahash = {ca5d56881f8b30922f991466f807ec31}, journal = {International Journal of Advanced Computer Science and Applications(IJACSA)}, keywords = {; Analysis; Artificial Component Condition Maintenance Monitoring. Principal Remaining Wear life; network; neural prediction; scheduling useful}, number = 5, timestamp = {2014-02-21T08:00:08.000+0100}, title = {{Application of multi regressive linear model and neural network for wear prediction of grinding mill liners}}, url = {http://ijacsa.thesai.org/}, volume = 4, year = 2013 }