A robust cutting pattern recognition method for shearer based on Least Square Support Vector Machine equipped with Chaos Modified Particle Swarm Optimization and Online Correcting Strategy. (April 2020)

• Record Type: Journal Article
• Authors: Liu, Xinggao; He, Shuting; Gu, Youzhi; Xu, Zhipeng; Zhang, Zeyin; Wang, Wenhai; Liu, Ping
• Is Part Of: ISA transactions. Volume 99(2020)
• Page Start: 199
• Page End: 209
• Publication Date: 2020-04
• Subjects: Cutting pattern recognition -- Model mismatch -- Online Correcting Strategy (OCS) -- Least Square Support Vector Machine (LSSVM) -- Chaos Modified Particle Swarm Optimization algorithm (CMPSO); Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2019.08.069
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: Accurate cutting pattern recognition method for shearer in coal mining process has drawn more and more attention over the past decades due to its important role in guaranteeing the steady operation of the equipment, which, however, remains challenging caused by the mismatch of cutting pattern recognition especially for dynamic uncertainty of future sampled data. Therefore, a novel approach for cutting pattern recognition with an optimal Online Correcting Strategy (OCS) combined with Least Square Support Vector Machine (LSSVM) and Chaos Modified Particle Swarm Optimization (CMPSO) algorithm, named OCS-CMPSO-LSSVM, is proposed, where LSSVM models the functional relationship between input and output of the system, CMPSO optimizes the parameters of LSSVM, and OCS modifies the model to reduce its mismatch as the system runs, respectively. The performance of the proposed model is demonstrated with a simulation experiment and compared with the existing methods reported in the literature in detail. The experimental results reveal that the proposed models can achieve better cutting pattern recognition performance and higher robustness. Highlights: A novel cutting pattern recognition method based on LSSVM is proposed. CMPSO is applied to optimize the parameters of LSSVM. OCS is proposed to improve the model robustness in the uncertain environment. The proposed method has been experimentally validated. The proposed method has high recognition accuracy about 99.75%.