Abstract
Shearers play an important role in fully mechanized coal mining face and accurately identifying their cutting pattern is very helpful for improving the automation level of shearers and ensuring the safety of coal mining. The least squares support vector machine (LSSVM) has been proven to offer strong potential in prediction and classification issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. In this paper, an improved fly optimization algorithm (IFOA) to optimize the parameters of LSSVM was presented and the LSSVM coupled with IFOA (IFOA-LSSVM) was used to identify the shearer cutting pattern. The vibration acceleration signals of five cutting patterns were collected and the special state features were extracted based on the ensemble empirical mode decomposition (EEMD) and the kernel function. Some examples on the IFOA-LSSVM model were further presented and the results were compared with LSSVM, PSO-LSSVM, GA-LSSVM and FOA-LSSVM models in detail. The comparison results indicate that the proposed approach was feasible, efficient and outperformed the others. Finally, an industrial application example at the coal mining face was demonstrated to specify the effect of the proposed system.
Highlights
In a fully mechanized coal mining face, as the most important coal mining equipment, a shearer uses a drum to cut the coal
This paper utilizes the data-driven theory and proposes an intelligent identification method for shearer cutting patterns based on the integration of least squares support vector machine and an improved fruit fly optimization algorithm
This paper presents an improved fruit fly optimization algorithm to have shown that the least squares support vector machine (LSSVM) parameters have great influence on its learning and generalization ability
Summary
In a fully mechanized coal mining face, as the most important coal mining equipment, a shearer uses a drum to cut the coal. Due to the poor working conditions of coal mining, shearer operators does not have an accurate way to determine whether the shearer drum is cutting coal, rock, or coal with gangue depending only on simple visualization This can lead to some poor coal quality and low mining efficiency problems. Traditional identification techniques for shearer cutting patterns are mostly based on coal-rock recognition. These methods cannot satisfy the needs of practical applications and possess lower recognition rates because of the harsh conditions in practical production operation In this context, this paper refers to the fault diagnosis and pattern recognition methods for traditional equipment and focuses on the identification method for shearer cutting patterns.
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