Abstract
Shock signal features must be extracted for use in pattern recognition or fault diagnosis. In this work, we proposed a method for the feature extraction of shock signals, which are vibration signals that change faster and have larger amplitude ranges than general signals. First, we proposed the concepts of amplitude density for monotonic functions and piecewise monotonic functions. On the basis of these concepts, we then proposed the concept of the upper limit of density integral (ULDI), which was adopted to obtain signal features. Then, we introduced two types of serious fault cracks to the latch sheet of an automatic gun mechanism that is used on warships. Next, we applied the proposed method to extract the features of shock signals from data acquired when the automatic gun mechanism fired with normal and two fault patterns. Finally, we verified the effectiveness of our proposed method by applying the features that it extracted to a support vector machine (SVM). Our proposed method provided good results and was superior to the traditional statistics-based feature extraction method when applied to a SVM for classification. In addition, the former method demonstrated better generalisation than the latter. Thus, our method is an efficient approach for extracting shock signal features in pattern recognition and fault diagnosis.
Highlights
Shock signal features must be extracted for utilisation in pattern recognition or fault diagnosis
Using the diversity mutation, neighbourhood mutation, learning factor and inertia weight strategies for the basic particle swarm optimisation (PSO). They used the improved PSO algorithm to optimise the parameters of least squares support vector machines (LS-SVM) for the construction of an optimal LS-SVM classifier
Original features affect the accuracy of pattern recognition and fault diagnosis, and additional signal properties can be fully reflected by increasing the number of features
Summary
Shock signal features must be extracted for utilisation in pattern recognition or fault diagnosis. Deng et al [21] revealed the inherent characteristics of vibration signals by calculating the fuzzy information entropy values of IMFs and considering them as feature vectors. They proposed an improved particle swarm optimisation (PSO) algorithm by ISSN PRINT 1392-8716, ISSN ONLINE 2538-8460, KAUNAS, LITHUANIA. They used the improved PSO algorithm to optimise the parameters of least squares support vector machines (LS-SVM) for the construction of an optimal LS-SVM classifier. We analysed the features of shock signals for an automatic gun mechanism to verify the effectiveness of our proposed method
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