Research On Dynamic Maintenance For Multi-Unit Series-Parallel Systems Based On Intelligent Algorithm

Maintenance plays an important role in keeping availability and reliability levels of industrial equipment, weapons and transportation facilities, etc. In the face of increasingly fierce market competition, the enterprises are facing enormous pressure for cost reduction; maintenance cost has attracted attention of more and more enterprises as the largest single controllable cost. At present, corrective maintenance and preventive maintenance have been widely used in enterprises, which produce obvious effect. However, these maintenance strategies may lead to insufficient maintenance or over-maintenance which will bring serious economic loss to enterprises. Besides, most existing models are established for single-unit systems, which are not applicable to practical production systems consisting of multiple units. Therefore, reasonable maintenance decision has extremely vital significance in maintenance cost reduction and production efficiency improvement. In order to solve above problems, the thesis takes multi-unit series-parallel system as the research object, studies dynamic maintenance decision and scheduling method based on fault diagnosis and useful life prediction technology. The main contents are described as follows:A dynamic maintenance decision model for multi-unit series-parallel system is established considering performance degradation of units, economic dependence and structural dependence between units, and constraints of maintenance resources.The deterioration of units is modeled by Weibull distribution. Three maintenance actions, including minor repair, imperfect overhaul and replacement, are simultaneously considered to arrange the maintenance schedule of a system, maintenance cost include two parts: maintenance activity cost and downtime cost. Considering several maintenance activities in a time period and setup cost, an overall cost rate model is established.The genetic algorithm (GA) based methodology is employed to obtain the near optimal multi-unit maintenance scheduling which results in a relatively minimal maintenance cost rate. In the running process of the system, whenever a maintenance activity is generated, the GA will be called for solving the schedule activities.With respect to the optimization of thresholds in the model, an improved ant colony algorithm is proposed to solve the optimization problem in continuous space. This algorithm updates information according to random probability selection mechanism, through local search and global search process, finally finds the optimal solutions. After maintenance thresholds are optimized, we can ensure the system running smoothly at lower maintenance cost and less maintenance frequency.A maintenance simulation model is established under Flexsim environment, and real-time dynamic interaction of data between MATLAB maintenance decision model and Flexsim maintenance simulation model is realized. Then the influence of maintenance strategy on production system is analysised from equipment utilization, volume of production etc using Flexsim's powerful statistical function.Finally, the maintenance policy and simulation technology are applied to turbine blade production system. The results show the effectiveness and practicality of the maintenance strategy in reducing maintenance cost and improving production efficiency.