title: Cutting State Diagnosis for Shearer through the Vibration of Rocker Transmission Part with an Improved Probabilistic Neural Network.
abstract: In order to achieve more accurate and reliable identification of shearer cutting state, this paper employs the vibration of rocker transmission part and proposes a diagnosis method based on a probabilistic neural network (PNN) and fruit fly optimization algorithm (FOA). The original FOA is modified with a multi-swarm strategy to enhance the search performance and the modified FOA is utilized to optimize the smoothing parameters of the PNN. The vibration signals of rocker transmission part are decomposed by the ensemble empirical mode decomposition and the Kullback-Leibler divergence is used to choose several appropriate components. Forty-five features are extracted to estimate the decomposed components and original signal, and the distance-based evaluation approach is employed to select a subset of state-sensitive features by removing the irrelevant features. Finally, the effectiveness of the proposed method is demonstrated via the simulation studies of shearer cutting state diagnosis and the comparison results indicate that the proposed method outperforms the competing methods in terms of diagnosis accuracy.
authors.affiliation: School of Mechatronic Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China. lei.si@cumt.edu.cn.|School of Information and Electrical Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China. lei.si@cumt.edu.cn.
authors.forename: Lei
authors.initials: L
authors.lastname: Si
authors.affiliation: School of Mechatronic Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China. wangzbpaper@126.com.
authors.forename: Zhongbin
authors.initials: Z
authors.lastname: Wang
authors.affiliation: School of Mechatronic Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China. l_xinhua_2006@126.com.
authors.forename: Xinhua
authors.initials: X
authors.lastname: Liu
authors.affiliation: School of Mechatronic Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China. tccadcumt@126.com.
authors.forename: Chao
authors.initials: C
authors.lastname: Tan
authors.affiliation: School of Mechatronic Engineering, China University of Mining &amp; Technology, No. 1 Daxue Road, Xuzhou 221116, China. lin.zhang_2014@hotmail.com.
authors.forename: Lin
authors.initials: L
authors.lastname: Zhang
keywords: Kullback–Leibler divergence; distance-based evaluation; feature extraction; fruit fly optimization algorithm; probabilistic neural network; shearer cutting state diagnosis