High-speed automaton is core component of small caliber artillery, because of its poor working condition, the crack and wear of each component and its working reliability have gradually become the focus of fault monitoring and diagnosis. This traditional test method (mainly used in the field of weapon) not only needs a lot of cost and time, but also is vulnerable to many uncertain factors. Therefore, this paper uses modern test and analysis method collecting automaton vibration signal during shooting action and applying signal processing methods to extract features susceptible to fault, so as to identify the fault. Considering the high-speed automatic movement process and its nonlinear vibration signal, short time, transient, impact properties. In order to make fault information to be highlighted, firstly, according to the automaton movement patterns decomposition and time cycle diagram, the time-domain signal peak corresponds to collision between parts, and vibration signal corresponding to the motion of fault component is intercepted as the analysis object. Then, wavelet threshold de-noising method is used to preprocess the signal, making the open atresia impact obviously. Secondly, In order to comprehensively measure signal fractal characteristics, the override method is used to calculate the vibration signal generalized fractal dimension and draw the generalized fractal dimension spectrum, box dimension, information dimension, correlation dimension automaton as fault feature values are extracted. Then quantitative diagnosis index at the level of feature information integration --the index distance of three demensional characteristic parameters is proposed. In view of the fault feature parameters extracted under various conditions, We compute the average respectively, then obtain the four standard centers separately representing automaton four conditions in three dimensional space. In view of the vibration signals to be detected, according to the extracting three-dimensional characteristic parameters, we can find the corresponding characteristic index points in the three-dimensional space, respectively calculate these distances of between the characteristic index points and four standard centers, the index distance of three demensional characteristic parameters, and draw graphs of the index distance of three demensional characteristic parameters to identify fault conditions intuitively. Some identification errors are found in certain condition. In order to improve the deficiencies, we are determined to increase dimensions, increase fault characteristic parameters to identify conditions. So, singular spectrum entropy, power spectrum entropy, local wave spatial spectral entropy are extracted as quantitative features to describe the state changes of signal in time domain, frequency domain, time-frequency domain. Calculating the index distance of six-demensional characteristic parameters is suggested to identify conditions. Two graphs of the index distance of six-dimensional and three-dimensional characteristic parameters are drawed simultaneously to increase the comparative. Diagnosis results indicate that: the index distance of six-dimensional characteristic parameters can accuratly identify fault conditions of automaton, compared to the index distance of three-dimensional characteristic parameters. So, increasing the fault characteristic parameters and dimensions can improve the accuracy of fault identification. Also, multi-fractal theory and information entropy are sensitive to extract fault characteristic values. This paper provides a new idea for fault diagnosis of automaton.
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