Predicting mill load using partial least squares and extreme learning machines

Online prediction of mill load is useful to control system design in the grinding process. It is a challenging problem to estimate the parameters of the load inside the ball mill using measurable signals. This paper aims to develop a computational intelligence approach for predicting the mill load. Extreme learning machines (ELMs) are employed as learner models to implement the map between frequency spectral features and the mill load parameters. The inputs of the ELM model are reduced features, which are extracted and selected from the vibration frequency spectrum of the mill shell using partial least squares (PLS) algorithm. Experiments are carried out in the laboratory with comparisons on the well-known back-propagation learning algorithm, the original ELM and an optimization-based ELM (OELM). Results indicate that the reduced feature-based OELM can perform reasonably well at mill load parameter estimation, and it outperforms other learner models in terms of generalization capability.