Title: Data-driven automatic identification of cyclical final resistance of hydraulic support based on fuzzy trend characteristics of time series data

Authors: Huaiwei Ren; Shixin Gong; Yibo Du; Guorui Zhao

Addresses: Intelligent Mining Branch, CCTEG Coal Science and Technology Research Institute, Beijing, 100013, China; Mining Design Division, Tiandi Technology Co., Ltd., Beijing, 100013, China ' Intelligent Mining Branch, CCTEG Coal Science and Technology Research Institute, Beijing, 100013, China; Mining Design Division, Tiandi Technology Co., Ltd., Beijing, 100013, China ' Intelligent Mining Branch, CCTEG Coal Science and Technology Research Institute, Beijing, 100013, China; Mining Design Division, Tiandi Technology Co., Ltd., Beijing, 100013, China ' Intelligent Mining Branch, CCTEG Coal Science and Technology Research Institute, Beijing, 100013, China; Mining Design Division, Tiandi Technology Co., Ltd., Beijing, 100013, China

Abstract: To achieve accurate online identification of cyclical final resistance of hydraulic support for the analysis of strata behaviour regularity and lay the foundation for data awareness in the intelligent mining mode, we propose an identification and extraction scheme of cyclical final resistance of hydraulic support based on data fuzzy trend characteristics. Specifically, the trend characteristics of the working resistance time series data in a sliding window are marked and processed based on fuzzy vector mapping, which can realise data compression and trend information retention. Then a trend identification model based on the decision tree algorithm is established and different trend types of working resistance are identified based on the determined classification thresholds. Finally, accurate automatic identification of cyclical final resistance of hydraulic support is achieved. The effectiveness and feasibility of the proposed scheme are validated by practical data, and the results show that the identification accuracy is obviously increased by at least 13%.

Keywords: hydraulic support; cyclical final resistance; time series data; fuzzy vector mapping; intelligent mining; identification.

DOI: 10.1504/IJMME.2023.133649

International Journal of Mining and Mineral Engineering, 2023 Vol.14 No.2, pp.180 - 204

Received: 28 Nov 2021
Received in revised form: 28 Oct 2022
Accepted: 02 Nov 2022
Published online: 27 Sep 2023 *

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