2012 ©
Journal Publication
Title of Article The Predictable Maintenance 4.0 by Applying Digital Technology: A Case Study of Heavy Construction Machinery 
Date of Acceptance 20 August 2018 
     Title of Journal Review of Integrative Business and Economics Research (RIBER)  
     Standard OTHER (ProQuest) 
     Institute of Journal The Society of Interdisciplinary Business Research (SIBR)  
     Volume 2019 
     Year of Publication 2018 
     Page 34-46 
     Abstract This paper aimed to 1) classify the heavy machinery working process and 2) analyze vibration for the heavy machinery working process (Vibration Monitoring and Analysis) in order to apply the results for future planning of predictable maintenance using heavy machinery. The data was collected from grouping engines through analysis of life expectancy, duties, importance and vibration from 14 heavy machinery pieces, during which value changes were observed that were the causes of deterioration to synthesize the data for predicting maintenance from a smartphone application and analyzing the forecast by finding the appropriate time to inspect heavy machinery. It was discovered that every 15 days and 30 days were the most appropriate times for checking conditions. After preserving machines in good working condition, categorization was carried out by the working processes of different lengths of time, resulting in more accurate preventive schedules that can be constructed by using data checking rather than average number of machine breakdowns. However, the policy was set to maintain heavy machinery as the result of the process from Mean Time between Failure (MTBF) values together with Mean Time to Repair (MTTR) values, which reduce damage by applying 4.0 digital technologies to predict maintenance in heavy machinery.  
     Keyword Keywords: The Predictable maintenance applying 4.0 digital technologies, Heavy machinery, Vibration monitoring and analysis 
595740136-1 Miss VIEWWIKA KLATHAE [Main Author]
College of Graduate Study in Management Master's Degree

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citation true 
Part of thesis true 
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