A Novel Intelligent Decision-Making Method of Shearer Drum Height Regulating Based on Neighborhood Rough Reduction and Selective Ensemble Learning

Abstract

An intelligent shearer drum height regulating method is the key technology for mining at an unmanned coalface. In this study, a novel intelligent decision-making method of shearer drum height regulating is proposed, which makes a decision by selective ensemble the Kernel Extreme Learning Machine (KELM) with a self-learning ability. In this approach, the shearing coal process of the shearer is characterized based on the extended finite state machine. Transfer attributes are introduced to establish the decision information system of shearer drum height regulating. Then, propose a neighborhood rough reduction method is proposed to generate distinctive attribute subsets, which is applied to train the base classifiers based on the online KELM. Finally, we introduce an accuracy-guided forward search and post-pruning strategy to select part of the base classifiers for constructing an efficient and effective ensemble system of the shearer drum lifting prediction. For evaluating the proposed method, four evaluation metrics are used: accuracy, precision, recall rate and the F1-score, which are the most popular metrics for evaluating the performance of a classifier. We use the ten-fold cross validation method to optimize the hyperparameters. The proposed method is compared in two different scenarios: 1) three different classes of base classifier algorithms which including the Support Vector Machines (SVM), Support Vector Machines (CART) and K-NearestNeighbor (KNN) are used, and 2) two traditional ensemble methods including the bagging and random subspace. The proposed method is performed on the field datasets and the experimental results reveal that the method is effective in comparison to other approaches for shearer drum lifting prediction.