Condition-Based Maintenance Scheduling Strategies And System Implementation Of Heavy Equipment

Heavy equipments such as concrete-pump car,that’s performance data have a direct impact on reliability of operation and maintenance cost,are widely used in industrial engineering such as building construction, hydraulic engineering construction, etc. As time went by, there is degradation of heavy equipments performance for reasons of vibration,pollution, attrition. Maintenance activities can recover equipments performance and increase system reliability, but the unreasonable ones can produce a very great amount of excess or short maintenance. At present, SANY Ltd. monitors performance parameter of engineering machinery that SANY produced, but SANY haven’t analyzed the monitoring data effectively which can guide the maintenance activities. To solve this problem and to reduce maintenance cost, this paper does research on key technology on making maintenance planning and developing relevant system based on the monitoring performance parameter of equipments.The monitoring signal contains a lot of noise on account of poor working conditions of large complex equipments, and while the predicting results of equipments health status are accurate, to decrease the loss of effective information that de-noising technology brings, this paper does research on fuzzy technology based on interval number and forecasting techniques based on linear fusion. While ensuring accurate results of prediction, the value of performance parameter is substituted by interval number in the fuzzy technology to avoid harmful impacts upon forecast result by random noise; linear-fusion prediction technology trains weight between status models of a lot of similar equipments based on Genetic Algorithm, and weighted fusion algorithm combines these similar equipments to get base model of equipments health status for predicting.Maintenance planning model is the key to reduce maintenance cost. This paper builds Best Maintenance Time Model for one equipment and Maintenance Planning Model for more equipments considering real factors which impacts on maintenance cost. Best Maintenance Time Model that ensure maintenance activities can decrease the running cost, is a Single-objective nonlinear model that takes maintenance time as variable and minimum running cost per unit time as target function. Maintenance Planning Model that consider the loss of stop line, early or later maintenance, etc., is a multiple-objective nonlinear model that takes maintenance time of every equipment as variables and minimum maintenance cost as target function, to realize making maintenance planning of the group.The research on algorithms of model is to solve maintenance model efficiently. Simulated Annealing Algorithms can jump out of local minimum point and the convergence speed of Particle Swarm Algorithms is fast, so the paper uses Simulated Annealing Algorithms to solve Best Maintenance Time Model of one equipment and Particle Swarm- Simulated Annealing mixed algorithms to solve Maintenance Planning Model of more equipments. The algorithms are demonstrated with the example and data from other equipments, to prove that it is efficient, accurate.Finally, the paper develops maintenance planning strategies system based on requirements of SANY and the above-mentioned theories. Establish information model, function model,architecture of system, and develop six function modules: data input module, data preprocess module, fusion prediction module, maintenance planning for single equipment module, maintenance planning for multiple equipments module, user management module, to support SANY Heavy Industries *** Co., Ltd. in information management and maintenance activities on maintenance, repair, operation.