The spontaneous combustion is a smoldering process and characterized by a slow burning speed and a long duration. Therefore, it is a hazard to coal mines. Early detection of coal mine spontaneous combustion is quite d...The spontaneous combustion is a smoldering process and characterized by a slow burning speed and a long duration. Therefore, it is a hazard to coal mines. Early detection of coal mine spontaneous combustion is quite difficult because of the complexity of different coal mines. And the traditional threshold discriminance is not suitable for spontaneous combustion detection due to the uncertainty of coalmine combustion. Restrictions of the single detection method will also affect the detection precision in the early time of spontaneous combustion. Although multiple detection methods can be adopted as a complementarity to improve the accuracy of detection, the synthesized method will in- crease the complicacy of criterion, making it difficult to estimate the combustion. To solve this problem, a fuzzy inference system based on CRI （Compositional Rule of Inference） and fuzzy reasoning method FITA （First Infer Then Aggregate） are presented. And the neural network is also developed to realize the fuzzy inference system. Finally, the effectiveness of the inference system is demonstrated bv means of an experiment.展开更多
Construction of metro tunnels in dense and crowded urban areas is faced with many risks, such as sub- sidence. The purpose of this paper was the prediction of subsidence risk by failure mode and effect anal- ysis （F...Construction of metro tunnels in dense and crowded urban areas is faced with many risks, such as sub- sidence. The purpose of this paper was the prediction of subsidence risk by failure mode and effect anal- ysis （FMEA） and fuzzy inference system （FIS）. Fuzzy theory will be able to model uncertainties. Fuzzy FMEA provides a tool that can work in a better way with vague concepts and without sufficient informa- tion than conventional FMEA. In this paper, S and D are obtained from fuzzy rules and 0 is obtained from artificial neural network （ANN）. FMEA is performed by developing a fuzzy risk priority number （FRPN）. The FRPN for two stations in Tehran No.4 subway line is 3.1 and 5.5, respectively. To investigate the suit- ability of this approach, the predictions by FMEA have been compared with actual data. The results show that this method can be useful in the prediction of subsidence risk in urban tunnels.展开更多
基金Project 20050290010 supported by the Doctoral Foundation of Chinese Education Ministry and 2005AA133070 by National 863 Program for High Technique Research Development
文摘The spontaneous combustion is a smoldering process and characterized by a slow burning speed and a long duration. Therefore, it is a hazard to coal mines. Early detection of coal mine spontaneous combustion is quite difficult because of the complexity of different coal mines. And the traditional threshold discriminance is not suitable for spontaneous combustion detection due to the uncertainty of coalmine combustion. Restrictions of the single detection method will also affect the detection precision in the early time of spontaneous combustion. Although multiple detection methods can be adopted as a complementarity to improve the accuracy of detection, the synthesized method will in- crease the complicacy of criterion, making it difficult to estimate the combustion. To solve this problem, a fuzzy inference system based on CRI （Compositional Rule of Inference） and fuzzy reasoning method FITA （First Infer Then Aggregate） are presented. And the neural network is also developed to realize the fuzzy inference system. Finally, the effectiveness of the inference system is demonstrated bv means of an experiment.
文摘Construction of metro tunnels in dense and crowded urban areas is faced with many risks, such as sub- sidence. The purpose of this paper was the prediction of subsidence risk by failure mode and effect anal- ysis （FMEA） and fuzzy inference system （FIS）. Fuzzy theory will be able to model uncertainties. Fuzzy FMEA provides a tool that can work in a better way with vague concepts and without sufficient informa- tion than conventional FMEA. In this paper, S and D are obtained from fuzzy rules and 0 is obtained from artificial neural network （ANN）. FMEA is performed by developing a fuzzy risk priority number （FRPN）. The FRPN for two stations in Tehran No.4 subway line is 3.1 and 5.5, respectively. To investigate the suit- ability of this approach, the predictions by FMEA have been compared with actual data. The results show that this method can be useful in the prediction of subsidence risk in urban tunnels.
