Sudden Large Fault Self-organization Critical State Risk Measurement And Early Warning Research

The research of this thesis originates from two programs, one is the National Natural Science Foundation program (70701007) in2007--Project relate to the Dynamic Uncertain Planning Capacity Prediction and the Research of Risk Decision. The other is the National Natural Science Foundation Item (51075060) in2010--Self-organized critical state identification and research of risk measurement about equipment’s sudden large failure. The former aims to evaluate and control the risk based on excessive and insufficient plan through the anticipation of capacity planning, the planning, which is from the research of the uncertain factors during the capacity planning process. The latter is mainly to research the judgment, identification, and risk measurement of self-organized critical state about sudden faulted equipment. This thesis aims to carry out an in-depth research based on the risk measurement achievement of the former item, thus to solve the risk measurement problem of the critical big-sudden-faulted equipment. And paper is also based on the equipment’s self-organized critical state identification of the semiconductor manufacturing enterprise, to analysis the identified self-organized critical state risk factor of equipment, and thus to measure the risk of critical big-sudden-faulted equipment.The purpose of this thesis is to control the damages that caused by equipment’s sudden large failure in the random dynamic uncertain production environment. In order to control the disadvantage consequence during the productive process within customer’s acceptable range, the thesis needs to make risk measurement to self-organized critical state identification result of equipment’s sudden failure. So the paper based on the characteristic research of equipment’s sudden large failure, proposes the assumption of self-organized criticality mechanism that correspond with equipment’s sudden large failure, and has proved the assumption from the two aspects of qualitative analysis and quantitative research, and the thesis also introduces the investment risk measurement theory (CVaR risk measurement model) of the financial field into the risk measurement field of equipment’s sudden large failure, which is proved to be reliable and valid. Thesis discovered the internal relationship between Emergency Chain analysis method and FTA about the geodynamic mechanism when researched the risk identification of equipment’s sudden large failure, based on this and the two methods above, the paper proposed an advantage complementary method of EC-FTA risk factor identification and sequencing according the importance degree, and did the empirical research of this advantage complementary method by using the EC-FAT analysis method to the semiconductor manufacturing enterprise. It obtains the algorithms that are coincident with CVaR risk measurement of fractal distribution by improving the CVaR risk measurement algorithms of normal distribution, and makes risk metric modeling and empirical research of the equipment’s big-sudden-faulted case, and this improved algorithms also proved to be reliable and ascendant. This thesis proposed risk process controlling of equipment’s sudden large failure and decision model pertinently, and also did the research of risk warning on equipment’s sudden large failure with the introduction of BP neural network model at the same time.