THE IMPACT OF DIGITAL TWIN CAPABILITIES ON PREDICTIVE MAINTENANCE PERFORMANCE: AN ANALYTICAL STUDY
Keywords:
digital twin capabilities, predictive maintenance performanceAbstract
This research investigates how the use of digital twin capabilities will enhance the predictive maintenance performance in the Samarra Thermal Power Plant. The focus is placed upon five key dimensions of digital twin capability; that being real-time monitoring, digital simulation, fault prediction, intelligent decision-making and digital integration. Descriptive analysis was used to analyze all the relationships outlined by this study. The population for this study comprised 113 technical managers, engineers and programmers. From these, a stratified random sample of 70 respondents was chosen. Electronic questionnaires based upon previously relevant studies were distributed to collect data.
A number of statistical methods were used in the study with the help of SPSS version 24 and Microsoft Excel to analyze the information gathered from the participants. Descriptive statistics, Pearson's correlation analysis and Ordinary Least Squares (OLS) regression analysis are some examples of these statistical methods. In accordance with the findings, there is an extremely high positive correlation between Digital Twin Capability and Predictive Maintenance Performance. Additionally, it has been found that each dimension of Digital Twin Capability positively impacts Predictive Maintenance Performance; this demonstrates the capability of Digital Twins to enhance Fault Prediction Accuracy, Reduce Unplanned Downtime, Lower Maintenance Costs, Enhance Asset Reliability and Increase Equipment Lifecycle.
Recommendations made by this research are that there is a need for increased use of digital twin technologies across industrial organizations, as well as an increase in the degree of integration of these technologies into other forms of digital technology to enhance the maintenance effectiveness and reliability of operations. This research provides original data on the extent to which Digital Twin Capabilities can be used to provide improvements in Predictive Maintenance Performance within the Iraq Energy Sector.
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