Damage Location Diagnosis of Frame Structure Based on a Novel Convolutional Neural Network

In the case of strong noise, when the damage occurs at different locations of the frame structure, the fault vibration signals generated are relatively close. It is difficult to accurately diagnose the specific location of the damage by using the traditional convolution neural network method. In order to solve this problem, this paper proposes a novel convolutional neural network. The method first uses wavelet decomposition and reconstruction to filter out the noise signal in the original vibration signal, then uses CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Analysis) to decompose the filtered signal to highlight the feature information in the filtered signal. Finally, a convolution neural network combined with WDCNN (First Layer Wide Convolution Kernel Deep Convolution Neural Network) and LSTM (Long Short-Term Memory Network) is used to achieve the accurate classification of the signal, so as to achieve the accurate diagnosis of the damage location of the frame structure. Taking the four-story steel structure frame of Columbia University as the research object, the fault diagnosis method proposed in this paper is used to carry out experimental research under strong noise conditions. The experimental results show that the accuracy of the fault diagnosis method proposed in this paper can reach 99.97% when the signal-to-noise ratio is −4 dB and the objective function value is reduced to 10−4. Therefore, the fault diagnosis method proposed in this paper has a high accuracy in the strong noise interference environment; it can realize a high precision diagnosis of the damage location of the frame structure under a strong noise environment. The contribution and innovation of this paper is to propose a novel fault diagnosis method based on the convolutional neural network, which solves the problem of accurate damage location diagnosis of frame structures under strong noise environment.