Equipment utilized in intelligent underground coal mines necessitates clear distinction from general equipment due to its unique exposure to the specialized subterranean environment. This exposure results in distinct failure patterns, usage, and maintenance protocols. Historically, issues such as incomplete failure data records have impeded accurate failure prediction, leading to a reliance on experience-based maintenance practices for underground mining equipment. To enhance the accuracy of fault prediction for coal mine machinery, this paper proposes a two-stage fault prediction method based on Grey–Markov method. Leveraging daily maintenance data of intelligent dispatching center, this study employs a Grey model to generate initial failure rate forecasts, followed by the integration of the Markov model to refine prediction accuracy. Based on the daily maintenance data from the intelligent dispatching center, the anchor digging integrated machine and conveyor are selected as the case study equipment. The results show that the prediction accuracy of Grey–Markov model is higher than that of the control group.
ZhangGCaoXZhangM.A knowledge graph system for the maintenance of coal mine equipment. Math Probl Eng2021; 2021: 2866751.
2.
LiDRenMMengG.Application of internet of things technology on predictive maintenance system of coal equipment. In: 13th Global Congress on Manufacturing and Management (GCMM), Zhengzhou Univ, Zhengzhou, PEOPLES R CHINA, Conference 28–30 November 2017, pp. 885-889, Zhengzhou: Zhengzhou University.
3.
Al-DuaisFSMohamedABAJawaTM, et al. Optimal periods of conducting preventive maintenance to reduce expected downtime and its impact on improving reliability. Comput Intell Neurosci2022; 2022: 7105526.
4.
ZhangXYuYXiongF, et al. Prediction of daily blood sampling room visits based on ARIMA and SES model. Comput Math Methods Med2020; 2020: 1720134.
5.
SidqiFSumitraID, Iop. Forecasting product selling using single exponential smoothing and double exponential smoothing methods. In: 2nd International Conference on Informatics, Engineering, Science, and Technology (INCITEST) - Building Competitive Advantage to Face Industry 4.0, Bandung, INDONESIA, Conference 18 July 2019, Paper no. 032031, pp.1–6, Bristol: IOP Publishing.
6.
WangZZhangMWangD, et al. Failure prediction using machine learning and time series in optical network. Opt Express2017; 25(16): 18553–18565.
7.
MyrzatayARzayevacLBandiniS, et al. Predicting LAN switch failures: an integrated approach with DES and machine learning techniques (RF/LR/DT/SVM). Results Eng2024; 23: 1–14.
8.
De MarcoLMTrierweilerJOFarenzenaM.Determination of remaining useful life in cyclic processes. Ind Eng Chem Res2019; 58(48): 22048–22063.
9.
WangZWuXWangH, et al. Prediction and analysis of domestic water consumption based on optimized grey and Markov model. Water Supply2021; 21(7): 3887–3899.
10.
OzdemirAOzdagogluG.Predicting product demand from small-sized data: grey models. Grey Syst Theory Appl2017; 7(1): 80–96.
11.
XiongPZengXWuL, et al. A fluctuation data grey model and its prediction of rainstorm days. Appl Math Modell2024; 127: 767–783.
12.
ZhangKYinKYangW.Probabilistic accumulation grey forecasting model and its properties. Expert Syst Appl2023; 223: 119889.
13.
HuZLiuC.An improved nonequidistant grey model based on Simpson formula and its application. Complexity2021; 2021: 6654324.
14.
ChenHSunXFuL, et al. Prediction of industrial electricity consumption based on grey cluster weighted Markov model. Int J Mod Phys C2024; 35(10): 2450130.
15.
WangHWangG.A novel gray model based on normalized beta function and its applications. Commun Stat Simul Comput2023; 52(8): 3724–3740.
16.
RabinerLR.A tutorial on Hidden Markov-models and selected applications in speech recognition. Proc IEEE1989; 77(2): 257–286.
He Yong BaoY. Grey-Markov forecasting model and its application. Syst Eng Theory Pract1992; 12(4): 59–63.
19.
XuYLinTDuP.A hybrid coal prediction model based on grey Markov optimized by GWO: a case study of Hebei provinceExpert Syst Appl2024; 235: 121194.
20.
XuJWangWGaoZ, et al. A novel Markov model for near-term railway delay prediction. Comput Ind Eng2023; 181: 109302.
21.
LuoJKleinTJiQ, et al. Forecasting realized volatility of agricultural commodity futures with infinite Hidden Markov HAR models. Int J Forecasting2022; 38(1): 51–73.
22.
OlivaresGTeferleFN.A Bayesian monte Carlo Markov chain method for parameter estimation of fractional differenced gaussian processes. IEEE Trans Signal Process2013; 61(9): 2405–2412.
23.
ZhangJZhangYFuJ, et al. Capacity fading knee-point recognition method and life prediction for lithium-ion batteries using segmented capacity degradation model. Reliab Eng Syst Saf2024; 251: 110395.
24.
RaiAKimJ-MKumarA, et al. Gear fault diagnosis based on bidimensional time-frequency information theoretic features and error-correcting output codes: a multi-class support vector machine. Proc IMechE, Part O: J Risk and Reliability2025; 239(3): 552–567.
25.
QuYFuSYongM, et al. Health indicator construction and remaining useful life prediction based on MSC-LSTM-AE model for working bearings. IEEE Sens J2025; 25(9): 15525–15535.
26.
WuJHuangX.Application research of lifting wavelet transform in fault diagnosis of coal mine bearing. J Mine Autom2016; 42(9): 74–76. (IN CHINA)
27.
ZhangMXuTSunH, et al. Fault diagnosis of mine hoist based on fuzzy fault tree and Bayesian network. J Mine Automa2020; 46(11): 1–5+45. (IN CHINA)
28.
HanCZouGYehH-G, et al. Intelligent fault prediction with wavelet-SVM fusionin coal mine. Comput Geosci2025; 194: 105744.
29.
ZhangHChenY.Analysis and application of grey-Markov chain model in tax forecasting. J Math2021; 2021(1): 1–11.
30.
ZhouXGuoLZhangJ, et al. Prediction of mine dust concentration based on grey Markov model. Shock Vib2021; 2021: 5859249.
31.
WuLZLiSHHuangRQ, et al. A new grey prediction model and its application to predicting landslide displacement. Appl Soft Comput2020; 95: 106543.
32.
ZhaoGZhangHQinX, et al. Prediction of grain humidity based on improved grey Markov model. In: IEEE symposium series on computational intelligence (SSCI), Xiamen, PEOPLES R CHINA, Conference 6–9 December 2019, pp.2933–2939. New York, NY: IEEE.
33.
BalogunA-LRezaieFQuoc BaoP, et al. Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression (SVR) with GWO, BAT and COA algorithms. Geosci Front2021; 12(3): 101104.
34.
HuangPWenCFuL, et al. A hybrid model to improve the train running time prediction ability during high-speed railway disruptions. Saf Sci2020; 122: 104510.
