Conveyor Belts Joints Remaining Life Time Forecasting with the Use of Monitoring Data and Mathematical Modelling

Monitoring the condition of conveyor belt joints by measuring and continuously analysing changes in their length raises a number of research challenges. One of them is the need for a more advanced method of assessment of the condition of each individual joint. Suitable mathematical modelling that would allow to identify the change in the length of such joints over its life time could be considered as a potential solution. Therefore, the aim of this study was to indicate that on the basis of the operation history of such elements, it is possible to estimate the parameters of the time series model, allowing to identify the joints which should be observed more carefully due to the unfavourable changes occurring in them. As a results of this research aim - the article presents the methodology allowing to identify the proper model and the function estimated for the selected joint.

[1]  Dariusz MAzurkiewicz.  Computer-aided maintenance and reliability management systems for conveyor belts , 2014 .

[2]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1972 .

[3]  R. Chou,et al.  ARCH modeling in finance: A review of the theory and empirical evidence , 1992 .

[4]  R. Engle.  Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[5]  Dariusz Mazurkiewicz,et al.  Laboratory measurements of vehicle exhaust emissions in conditions reproducing real traffic , 2021 .

[6]  E. Kissi,et al.  Forecasting Construction Tender Price Index in Ghana using Autoregressive Integrated Moving Average with Exogenous Variables Model , 2018 .

[7]  Jarosław Selech,et al.  An aggregate criterion for selecting a distribution for times to failure of components of rail vehicles , 2019 .

[8]  T. Breurch,et al.  A simple test for heteroscedasticity and random coefficient variation (econometrica vol 47 , 1979 .

[9]  D. Valis,et al.  Modelling of a transport belt degradation using state space model , 2017, 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM).

[10]  J. Wooldridge.  Introduction to Econometrics , 2013 .

[11]  Edward Kozłowski.  Analiza i identyfikacja szeregów czasowych , 2015 .

[12]  Dariusz Mazurkiewicz,et al.  Assessment model of cutting tool condition for real-time supervision system , 2019, Eksploatacja i Niezawodnosc - Maintenance and Reliability.

[13]  Dezhen Yang,et al.  A novel reliability estimation method of multi-state system based on structure learning algorithm , 2019 .

[14]  David Valis,et al.  Application of selected Levy processes for degradation modelling of long range mine belt using real-time data , 2018 .

[15]  P. Kokoszka,et al.  KPSS test for functional time series , 2016 .

[16]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[17]  Funda Iscioglu,et al.  Dynamic reliability analysis of a multi-state manufacturing system , 2019 .

[18]  Anil K. Bera,et al.  ARCH Models: Properties, Estimation and Testing , 1993 .