Identification of Mode Shapes of a Composite Cylinder Using Convolutional Neural Networks

Abstract

The aim of the following paper is to discuss a newly developed approach for the identification of vibration mode shapes of multilayer composite structures. To overcome the limitations of the approaches based on image analysis (two-dimensional structures, high spatial resolution of mode shapes description), convolutional neural networks (CNNs) are applied to create a three-dimensional mode shapes identification algorithm with a significantly reduced number of mode shape vector coordinates. The CNN-based procedure is accurate, effective, and robust to noisy input data. The appearance of local damage is not an obstacle. The change of the material and the occurrence of local material degradation do not affect the accuracy of the method. Moreover, the application of the proposed identification method allows identifying the material degradation occurrence.

Detection of Material Degradation of a Composite Cylinder Using Mode Shapes and Convolutional Neural Networks

“…To overcome the limitations of the traditional approaches (2D analysis, high spatial resolution of mode shapes description), CNNs are applied to create a tool to analyze the mode shapes of vibrations and to draw conclusions about the appearance, location, and size of the material degradation zone in the analyzed structure. This paper is a continuation of the research of the same authors (see [36]), where the CNN-based identification of mode shapes is presented. Herein, on the basis of identified mode shapes, the location and size of the material degradation zone are assessed.…”

“…If no degradation of the material is detected, the procedure ends. Otherwise, the identification of the degradation zone location and size is performed [36,63]. The whole procedure is shown in Figure 4 and can be described using three major steps:…”

“…This paper focuses on step 3, marked in Figure 4 with a yellow background. Steps 1 and 2 have been addressed in the previous paper by the same authors (see [36]); the accuracy of detection of the appearance of material degradation exceeding 99.8% (see Table 3) allows treating this step as practically error-free.…”

“…Table 3. CNN accuracy of the identification of the material degradation occurence (for details, see [36]).…”

“…Among these methods are, e.g., fractal dimension method [29], wavelet transform method [30] or Hilbert-Huang transform [31]. However, the mode shape-based approach lacks attention to the problem noted and analyzed in optimization problems: mode shapes crossing and mode shape identification, that is, the designation of the natural frequency and the corresponding vibration mode shape with a suitable "mode shape identifier" (see [32][33][34][35][36]).…”