To illustrate the metro braking performance more appropriately and comprehensively, and to explore the dynamic braking process more accurately, the consistency analysis was used in this paper, which is used for the first time in the metro brake field. Based on the stream data, several characteristic values (braking pressure setting time, maximum braking pressure and stability value of braking pressure, i.e.) were extracted to represent the dynamic process. Under similar braking conditions, such as the similar applied pressure, speed and brake level, etc., the performance of brake system is similar and stable in certain extent. The dynamic data model has been developed in this paper. Large data mining based statistics was used to analyze braking performance and the dynamic prediction during a braking cycle was provided. Investigation presented in this paper contributes to the efforts in the direction of metro braking systems performance and evaluation of service condition. The dynamic evaluation based on the stream data has been proved to provide a new way to explore the influence of metro braking system so that it is reasonable to analyze the service condition of metro brake system. Based on the LabVIEW platform, the system for analyzing stream data was built. Based on stream data, using the characteristic and statistics method, this paper provides a way to describe the service condition. The result enriches the evaluation criteria of metro service condition.

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