Identification and detection of the process fault in a cement rotary kiln by extreme learning machine and ant colony optimization, ISSN 1583-7904 - Kadri Ouahab; Mouss Leila Hayet (Academic Journal of Manufacturing Engineering, 2017)
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.