Digital Twin-driven Health Operation and Maintenance Strategies for Complex Equipment with Self-healing Capabilities

Abstract

With the development of the industrial level, the service-oriented transformation of the manufacturing industry has become a new growth point for the interests of the manufacturing industry recognized by all countries, and in this transformation process, the health operation and maintenance (HOM) of equipment has become a crucial link, especially for complex equipment, and its health operation and maintenance level directly affects the overall efficiency of the manufacturing industry. However, with the improvement of intelligence and complexity, a more general and complete set of methods and theories is needed for the healthy operation and maintenance of complex devices with self-healing states. Based on an in-depth analysis of the key issues of complex equipment health operation and maintenance, this paper introduces digital twin technology as a breakthrough. Digital twins provide new ideas for the healthy operation and maintenance of complex equipment. By defining a digital twin-driven PHM (Prediction and Health Management) framework, this paper not only solves the problem of mobility (i.e., virtual and real connection) of the cross-platform health operation and maintenance model, but also focuses on improving the accuracy and evaluation indicators of the model (the consistency between the virtual model and the entity). Addressing issues of strategy development and effectiveness evaluation. Based on the digital twin technology, a general model was established, and the self-healing phenomenon and different maintenance strategies were introduced to explore its impact on reliability. Based on the game idea, the reliability model is used to evaluate different maintenance strategies. So as to develop the optimal operation and maintenance strategy. This not only improves the accuracy and efficiency of O&M, but also provides a solid theoretical foundation and technical support for the intelligent and autonomous health O&M of complex equipment.