Affiliations: [a] School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, China
| [b] Shendong Coal Group Co., Ltd. of National Energy Group, Yulin, China
Correspondence:
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Corresponding author. Shuanfeng Zhao, School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an, 710001, China. E-mail: [email protected].
Abstract: The complexity of the coalface environment determines the non-linear and fuzzy characteristics of the drum adjustment height. To overcome this challenge, this study proposes an adaptive fuzzy reasoning Petri net (AFRPN) model based on fuzzy reasoning and fuzzy Petri net (FPN) and then applies it to the intelligent adjustment height of the shearer drum. This study constructs adaptive and reasoning algorithms. The former was used to optimize the AFRPN parameters, and the latter made the AFRPN model run. AFRPN could represent rules that had non-linear and attribute mapping relationships and could adjust the parameters adaptively to improve the accuracy of the output. Subsequently, the drum adjustment height model was established and compared to three models neural network (NN), classification and regression tree(CART) and gradient boosting decision tree (GBDT). The experimental results showed that this method is superior to other drum adjustment height methods and that AFRPN can achieve intelligent adjustment of the shearer drum height by constructing fuzzy inference rules.
Keywords: Drum intelligent adjustment, fuzzy reasoning, adaptive, Petri net
