Research Of On-line Life Prediction System For Large Crane

Life prediction for large crane is urgent for solving problems concerned by enterprise, and also an important subject of engineering and technique. As the design life does not match with the actual life, and crane actual operating conditions, it is significant to find a method which has sufficient accuracy and certain reliability to estimate residual life of large crane.As the crane working life is closely linked with the actual load, on-line monitoring technology with fatigue analysis theory was combined. A data acquisition system based on Compact RIO and an on-line life prediction system for large crane by LabVIEW were designed and developed.Then on-line monitoring and real-time display residual life were achieved, solving the practical problems in engineering.The achievement in domestic and international management was reviewed briefly ,and theoretical knowledge of fatigue was analyzed and summarized. For the large error in traditional fatigue life prediction, membership function to the life estimation was introduced in order to form fatigue life estimation method based on fuzzy revised Miner rule. Taking 32t-31.5m common bridge crane for example, fatigue life was estimated by online nominal stress method. With three-dimensional model established and finite element analysis preformed danger points were identified. Stress time data was calculated by rain-flow counting method and the S-N curve of the material was amended in order to obtain a revised S-N curve. At last, fuzzy revised Miner rule was used for predicting residual life use.In order to obtain the actual load of dangerous parts of cranes, a data acquisition system was designed, based on NI Compact RIO which had high reliability and configuration flexibility. The system was developed with LabVIEW and achieves data acquisition and storage.A large crane on-line life prediction system was established, materials and crane's operation condition were configurable. The material S-N curve data, gathering data, working conditions and residual life data were stored in database. Real-time transmission data was to perform life calculation, and displaying the result to the operator, in order to complete the crane's real-time monitoring and life prediction.As providing an accurate and practical resolution that can estimate the residual fatigue life of large crane, the method and software development were not only useful to bridge crane, but also apply to other cranes. The designer can expand software based on different devices and different operating conditions to meet a variety of special requirements.