A Method for Locating Tools in the Railway Moving Area Optimized Based on Received Signal Strength Indicator and a Fuzzy Neural Network

Abstract

To solve the problem that maintenance tools are difficult to retrieve once being lost in maintenance of an electric section of the railway system, this study proposed a new method for estimating the object location optimized by a fuzzy neural network based on received signal strength indicator. Through a radio frequency identification reader and antennae, the received signal strength indicator values feed back by the dropped tools were collected. The environmental attenuation factor was fitted with field data. In the pre-processing stage, K-means algorithm optimized by the genetic algorithm, thus improving accuracy of data. The processed data were trained and tested by the fuzzy neural network, so that they could more accurately estimate the actual location of dropped tools. Data were collected on the railway lines of National Local Joint Engineering Research Center of Safety Guarantee Technology for Operation and Maintenance of Rail Transport Infrastructure. The collected data were used for subsequent processing and locating tools. The experimental results show that the proposed location method offers advantages in improving the location accuracy of dropped tools, which can improve the retrieval efficiency of tools in the field.