Abstract
The safety computer in the train control system is designed to be the double two-vote-two architecture. If safety-critical multi-input data are inconsistent, this may cause non-strict multi-sensor data problems in the output. These kinds of problems may directly affect the decision making of the safety computer and even pose a serious threat to the safe operation of the train. In this paper, non-strict multi-sensor data problems that exist in traditional safety computers are analyzed. The input data are classified based on data features and safety computer features. Then, the input data that cause non-strict multi-sensor data problems are modeled. Fuzzy theory is used in the safety computer to process multi-sensor data and to avoid the non-strict multi-sensor problems. The fuzzy processing model is added into the onboard double two-vote-two architecture safety computer platform. The fuzzy processing model can be divided into two parts: improved fuzzy decision tree and improved fuzzy weighted fusion. Finally, the model is verified based on two kinds of data. Verification results indicate that the fuzzy processing model can effectively reduce the non-strict identical problems and improve the system efficiency on the premise of ensuring the data reliability.
Highlights
With the development of the railway field, the safety computer is playing an increasingly important role in the train control system [1,2,3,4]
The non-strict multi-sensor data problems of the safety computer can only be caused by dynamic data
The non-strict multi-sensor data problems that exist in traditional safety computers are analyzed
Summary
This paper is an extended version of our paper published in the Seventh International Conference on Control Automation & Information Sciences (ICCAIS), Hangzhou, China, 24–27 October 2018. Received: 14 December 2018; Accepted: 11 February 2019; Published: 17 February 2019
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