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This study concerns with fault diagnosis of urban rail vehicle auxiliary inverter using wavelet packet and RBF neural network. Four statistical features are selected: standard voltage signal, voltage fluctuation signal, impulsive transient signal and frequency variation signal. In this article, the original signals are decomposed into different frequency subbands by wavelet packet. Next, an automatic feature extraction algorithm is constructed. Finally, those wavelet packet energy eigenvectors are taken as fault samples to train RBF neural network. The result shows that the RBF neural network is effective in the detection and diagnosis of various urban rail vehicle auxiliary inverter faults.

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A relative detecting method is proposed to detect the synchronous error of several long stroke hydraulic cylinders by using a short stroke displacement sensor, which has the advantages of low cost, easy to install and convenient in operation compared with an absolute detecting one. A synchronous model test system is established and on-line detection and control is realized by utilizing computer technology. A strategy named as Fuzzy-PID control and a program developed for this system are used for compensation of the synchronous errors. An effective new method for detection and control of synchro operation of hydraulic cylinders is presented for a deep ocean mining heave compensation system.