A novel data driven decision-making framework for preventive maintenance and fault analysis in modern production systems

The high level of computerisation of modern production systems has led to the generation of large volumes of machining data, offering significant potential for using data-driven decision making (DDDM) for examining the fault analysis and incorporating efficient preventive maintenance measures. This research proposes a DDDM framework to incorporate data driven preventive maintenance and fault analysis in modern production systems. This framework uses IoT sensors, cloud computing, and machine learning for real-time fault analysis and preventive maintenance. This approach incorporates the analysis of failures using past machining data and suggests maintenance strategies based on fault analysis. The framework exhibits a continuous model improvement nature based on the system data by including continuous monitoring and model refinement loops. The failure mode effect analysis does systematic fault identification, predictive maintenance planning, and continuous improvement through feedback loops. The applicability of the framework is demonstrated through an experimental study on CNC turning of D2 alloy steel performed on a FANUC CNC lathe. The systematic fault analysis, visualisation of tool wear with machining parameters, capacity utilisation analysis, tool life prediction by multivariate linear regression machine learning model, failure analysis and FMEA were performed by the deep analysis of the recorded experimental data. A random forest model demonstrated an accuracy of 84.37% in tool failure prediction, effectively correlating predicted and actual tool life. Thus, by proposing framework and demonstrating its applicability in an industrial setup, this research establish theoretical as well as practical foundation for applying data driven preventive maintenance and fault analysis in modern production systems to enhance production quality and efficiency.