No abstract

It is difficult to effectively distinguish the key information of non-stationary dynamic signals in many engineering applications, such as fault detection, geological exploration, and logistics transportation. To deal with this problem, a classification and recognition algorithm based on variational mode decomposition (VMD) and the Support Vector Machine (SVM) optimized by the Whale Optimization Algorithm (WOA) optimization model is first proposed in this study. The algorithm first applies VMD to decompose the non-stationary time-domain signals into multiple variational intrinsic mode functions (VIMFs). Then, it calculates the correlation coefficient between each mode and the original signals and conducts signal reconstruction by sorting the VIMFs. On the base of this, it performs modal filtering on the non-stationary signals according to the correlation coefficients between the reconstructed signal and the original signal. Subsequently, the WOA is used to optimize two key parameters of the SVM. Finally, the optimization model is exploited to classify and recognize the impact and vibration of non-stationary signals. A series of simulations and experiments for the algorithm is carried out and analyzed deeply. The comparative test results indicate that the classification and recognition method for non-stationary signals based on VMD and WOA-SVM (VMD-WOA-SVM) proposed in this paper converges faster and recognizes the key information of non-stationary dynamic signals more accurately with a recognition precision of 96.66%.

The identification of key information hidden in non-stationary signals is challenging in various fields such as logistics and transportation, biomedicine, and fault diagnosis. To facilitate this identification, we propose a back propagation neural network (BPNN) classification and recognition algorithm based on wavelet threshold denoising (WTD) and manta ray foraging optimization (MRFO) algorithm for the first time.The algorithm first performs WTD on the original signals to obtain denoised signals. Subsequently, the MRFO algorithm is utilized to optimize the initial weights and thresholds of the BPNN. On the base of this, the optimization model is finally obtained to classify and recognize the key information in the non-stationary signals. The comparative experimental results indicate that the proposed WTD-MRFO-BPNN algorithm can be utilized to availably recognize the key information hidden in non-stationary signals. The recognition accuracy reaches 97.25%.

During the dynamic acquisition of impact signals, a high sampling frequency brings significant challenges to the analog-to-digital converter and other test systems. To address this issue, in this study, an improved compressed sensing (CS) method is proposed for the measurement of impact signals based on cubic spline interpolation (CSI). According to the characteristics of the dynamic impact signal, a random non-uniform sampling strategy combining CS and CSI is presented. The CSI obviously reduces the number of observation points required by the traditional CS. To resolve the problem that the traditional orthogonal matching pursuit (OMP) algorithm can only guarantee the local optimal solution but cannot obtain the global optimal solution, an improved orthogonal matching pursuit (IOMP) algorithm is proposed. First, n atoms related to residuals are selected to build a local atomic dictionary. Subsequently, the atom most relevant to the signal observation result is selected from the local atomic dictionary. The iteration process is repeated until enough atoms are selected. The IOMP algorithm effectively improves the success rate of reconstruction. Finally, an impact signals test platform based on the Machete hammer is established. The results of theoretical simulations and several experiments indicate that the data reconstruction error of the proposed improved CS method for impact signals is approximately 5.0%.