Optimizing Predictive and Prescriptive Maintenance Using Unified Namespace (UNS) for Industrial Equipment

The transition from the basic, reactive approach to maintenance toward the more sophisticated predictive and prescriptive maintenance options respond to key issues in industrial processes, including unforeseen halts, costly repairs, and poor resource management. Reactive maintenance focuses its efforts on post-failure action leading to interruptions as well as the imposition of significant economic costs while on the other end preventive maintenance, which often prompts additional unneeded activities alongside high costs. Predictive maintenance is useful in monitoring failures and its major disadvantage is that it may provide recommendations that are difficult to implement while prescriptive maintenance faces challenges such as integration of data, standardization of the process and lack of skilled professionals. To overcome these challenges, the proposed solution incorporates a Unified Namespace (UNS) architecture. UNS has become a real-time data management platform collecting data in various formats and providing intuitive workflow between devices. It is pivotal in the case of prognostic models that anticipate equipment breakdown and decision models that suggest choices that will result in better utilization of available resources, minimum time wastage, and high equipment reliability. Other applications for predictive maintenance include digital twins that improve the monitoring and testing processes required for sound decision making. Thus, the proposed UNS allows for deciphering integration and data standardization challenges and, therefore, contributes to scaling up the industrial maintenance practices.