Research On Intelligent Logic Model For Reliability Centered Maintenance And Fault Diagnosis

With the continuous development of technology,the structure of modern equipment has become increasingly complex,with,for example,an aircraft consisting of over a million components.In addition,there is a highly complex non-monotonic logical relationship between its faults and maintenance,often leading to multiple processes,multiple faults,and sudden faults,resulting in a huge demand for maintenance.Traditional maintenance strategies are ineffective in addressing the maintenance of modern equipment,resulting in maintenance work that falls short of expectations.To optimize the effectiveness of maintenance work,the Reliability Centered Maintenance(RCM)theory,which focuses on risk assessment and maintenance optimization,has been introduced into the maintenance of modern equipment.However,existing maintenance systems based on RCM theory have not fundamentally solved the problems of poor analysis accuracy,low calculation efficiency,and low level of intelligent analysis associated with RCM theory.In this paper,we address the problem of non-monotonic reasoning by applying Answer Set Programming(ASP)technology from the field of artificial intelligence and implement an intelligent logic model for RCM fault diagnosis and maintenance.To address the issues of the cumbersome and incomplete expression of RCM theory,low level of intelligent analysis,and lack of non-monotonic reasoning functions,this paper proposes the application of ASP technology for the construction of RCM fault diagnosis and maintenance model.By using ASP logical rules to describe the non-monotonic relationship between fault phenomena and maintenance strategies,not only can intelligent fault diagnosis and maintenance analysis processes be achieved,but also the relationship between faults and maintenance can be simplified,avoiding the ambiguity and incompleteness of natural language descriptions,resolving the multiple conclusion selection problem in system maintenance,and improving the reliability of fault diagnosis and maintenance.The specific application of the model is demonstrated using the example of aircraft engine faults,and the effectiveness of the model’s intelligent analysis is verified.To address the problem of poor accuracy of RCM theory analysis results,we added probabilistic logic reasoning based on qualitative and quantitative analysis to diagnose faults and make decisions,thereby improving the accuracy of model analysis results.We conducted consistency experiments from three different directions,and compared to the model without probabilistic logic reasoning,the accuracy of our model’s analysis results was improved about15%.To address the problem of low analysis efficiency and poor practicality of RCM theory,we carried out two rounds of data processing to improve the efficiency of fault diagnosis and maintenance work.First,we used ASP technology to formalize Window and realize data filtering.By matching fault information and excluding a large amount of irrelevant data from the knowledge base,the amount of data that needs to be traversed and accessed by the model during fault diagnosis and decision-making analysis is reduced,thereby reducing unnecessary repetitive work.Then,according to the maintenance needs of the equipment,we improved the preference algorithm of the asprin framework,selected optimization indicators,and set preference types to sort the remaining data,improving the computational efficiency of subsequent decision-making work and enhancing the practicality of our model.Simulation results show that the Window can exclude about 90% of irrelevant data,and the filtering effect is good.We also compared different models in terms of functionality and simulation run time.The experimental results show that our model has high computational efficiency and good practicality compared to the other two models.