基于 SaCE-ELM 的地铁牵引控制单元快速故障诊断

Metro traction control unit (TCU ) plays a key role in the operation of subway .It is important for the normal operation of subway to diagnose the TCU fault timely and effectively . However ,the traditional fault diagnosis methods usually have some disadvantages ,such as slow learning speed , falling into local optimum easily and poor prediction accuracy . To solve these problems ,extreme learning machine based on adaptive differential evolution algorithm (SaCE‐ELM ) is proposed .The input weights ,the implicit layer parameters and the output weights of the extreme learning machine are optimized by adaptive differential evolution algorithm .T he variation strategy of differential evolution algorithm is generated by the adaptive mechanism based on chaotic sequence ,and other parameters are randomly generated using normal distribution . The output weights of the network are calculated using Moore‐Penrose generalized inverse matrix . SaCE‐ELM doesn′t need artificial selection of variation strategy and parameters ,and its adaptive strategy is simpler than that of SaE‐ELM . Experimental results show that SaCE‐ELM has better prediction accuracy compared with E‐ELM ,SaE‐ELM ,LM‐NN and SVM .Moreover ,the training time of SaCE‐ELM is shorter than that of SaE‐ELM and SVM in all experimental datasets ,which demonstrates that the efficiency of model generation has been improved .