@article {4911,
	title = {Identification and detection of the process fault in a cement rotary kiln by extreme learning machine and ant colony optimization, ISSN 1583-7904},
	journal = {Academic Journal of Manufacturing Engineering },
	volume = {Vol 15},
	number = {Issue 2},
	year = {2017},
	abstract = {The aim of this paper is to propose a new fault diagnosis method for complex manufacturing system. We have used an artificial neural network (ANN) and an Ant Colony Optimization (ACO) algorithm to diagnosis the condition monitoring of a rotary cement kiln. The Ant Colony algorithm can found a small features subset from the original real time signals and the Extreme Learning Machine (ELM) enables a good accuracy with a limiting learning time. Many benchmark datasets have used to evaluate the performances of our algorithm and the result indicates its higher efficiency and effectiveness comparing to other methods.},
	url = {https://www.researchgate.net/publication/318838061_Identification_and_detection_of_the_process_fault_in_a_cement_rotary_kiln_by_extreme_learning_machine_and_ant_colony_optimization},
	author = {Kadri Ouahab and Mouss Leila Hayet}
}
