A predictive maintenance policy considering the market price volatility for deteriorating systems

2021 ◽  
Vol 162 ◽  
pp. 107686
Author(s):  
Hai-Canh Vu ◽  
Antoine Grall ◽  
Mitra Fouladirad ◽  
Khac Tuan Huynh
2017 ◽  
Vol 30 (3) ◽  
pp. 1242-1257 ◽  
Author(s):  
Yiwei WANG ◽  
Christian GOGU ◽  
Nicolas BINAUD ◽  
Christian BES ◽  
Raphael T. HAFTKA ◽  
...  

2019 ◽  
Vol 10 (4) ◽  
pp. 1993-2004
Author(s):  
Parth Pradhan ◽  
Shalinee Kishore ◽  
Boris Defourny

Author(s):  
David Adugh Kuhe

This study investigates the dynamic relationship between crude oil prices and stock market price volatility in Nigeria using cointegrated Vector Generalized Autoregressive conditional Heteroskedasticity (VAR-GARCH) model. The study utilizes monthly data on the study variables from January 2006 to April 2017 and employs Dickey-Fuller Generalized least squares unit root test, simple linear regression model, unrestricted vector autoregressive model, Granger causality test and standard GARCH model as methods of analysis. Results shows that the study variables are integrated of order one, no long-run stable relationship was found to exist between crude oil prices and stock market prices in Nigeria. Both crude oil prices and stock market prices were found to have positive and significant impact on each other indicating that an increase in crude oil prices will increase stock market prices and vice versa. Both crude oil prices and stock market prices were found to have predictive information on one another in the long-run. A one-way causality ran from crude oil prices to stock market prices suggesting that crude oil prices determine stock prices and are a driven force in Nigerian stock market. Results of GARCH (1,1) models show high persistence of shocks in the conditional variance of both returns. The conditional volatility of stock market price log return was found to be stable and predictable while that of crude oil price log return was found to be unstable and unpredictable, although a dependable and dynamic relationship between crude oil prices and stock market prices was found to exist. The study provides some policy recommendations.


Author(s):  
C. K. M. Lee ◽  
Yi Cao ◽  
Kam Hung Ng

Maintenance aims to reduce and eliminate the number of failures occurred during production as any breakdown of machine or equipment may lead to disruption for the supply chain. Maintenance policy is set to provide the guidance for selecting the most cost-effective maintenance approach and system to achieve operational safety. For example, predictive maintenance is most recommended for crucial components whose failure will cause severe function loss and safety risk. Recent utilization of big data and related techniques in predictive maintenance greatly improves the transparency for system health condition and boosts the speed and accuracy in the maintenance decision making. In this chapter, a Maintenance Policies Management framework under Big Data Platform is designed and the process of maintenance decision support system is simulated for a sensor-monitored semiconductor manufacturing plant. Artificial Intelligence is applied to classify the likely failure patterns and estimate the machine condition for the faulty component.


2013 ◽  
Vol 54 (4) ◽  
pp. 1033-1061 ◽  
Author(s):  
Carl Brousseau ◽  
Michel Gendron ◽  
Philippe Bélanger ◽  
Jonathan Coupland

Author(s):  
Wioletta Wróblewska ◽  
Eugenia Czernyszewicz

The aim of the study was to assess the level and volatility of prices of blueberry obtained in the farm (in wholesale on the domestic market and in export) and on the wholesale market during 2007-2016, due to choice of distribution channel. The level, direction and intensification of price changes were analyzed. The study shows that the prices of blueberry at the producer level were characterized by greater volatility than the wholesale market. Prices obtained by the producers on wholesale on the domestic market were significantly lower than in exports and in the wholesale market, on average in the analyzed period accounted for only 69% of the export price and 52% of the wholesale market price. Regardless of where the price comes from,the highest price for fruits was obtained in September, and the lowest in August, which is the month of the largest supply of fruits on the market.


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