High-speed automatic weapons play an important role in the field of national defense. However, current research on reliability analysis of automaton principally relies on simulations due to the fact that experimental data are difficult to collect in real life. Different from rotating machinery, a high-speed automaton needs to accomplish complex motion consisting of a series of impacts. In addition to strong noise, the impacts generated by different components of the automaton will interfere

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... each other. There is no effective approach to cope with this in the fault diagnosis of automatic mechanisms. This paper proposes a motion sequence decomposition approach combining modern signal processing techniques to develop an effective approach to fault detection in high-speed automatons. We first investigate the entire working procedure of the automatic mechanism and calculate the corresponding action times of travel involved. The vibration signal collected from the shooting experiment is then divided into a number of impacts corresponding to action orders. Only the segment generated by a faulty component is isolated from the original impacts according to the action time of the component. Wavelet packet decomposition (WPD) is first applied on the resulting signals for investigation of energy distribution, and the components with higher energy are selected for feature extraction. Three information entropy features are utilized to distinguish various states of the automaton using empirical mode decomposition (EMD). A gray-wolf optimization (GWO) algorithm is introduced as an alternative to improve the performance of the support vector machine (SVM) classifier. We carry out shooting experiments to collect vibration data for demonstration of the proposed work. Experimental results show that the proposed work in this paper is effective for fault diagnosis of a high-speed automaton and can be applied in real applications. Moreover, the GWO is able to provide a competitive diagnosis result compared with the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm.