Affiliations: [a] College of Computer Science and Technology, Huaqiao University, Xiamen, China | [b] State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University, Xi’an, China | [c] College of Sciences, San Diego State University, San Diego, CA, USA
Abstract: To address the problems of singularities, sensitivity to measurement noise, and low efficiency in traditional principal component analysis (PCA)-based operational modal analysis (OMA), we present a Sanger neural network principal component analysis (SNNPCA) algorithm to identify the operational modal parameters. SNNPCA is a two-layer neural network that is trained using a generalized Hebbian algorithm to ensure that its output converges to the principal components. After SNNPCA has converged, the link weights of SNNPCA correspond to the separation matrix of PCA. In SNNPCA-based OMA, the measurement response points are set as the input neurons, modal coordinate response signals are set as the output neurons, and the link weights of the neural network represent the modal shapes. Therefore, the operational modal identification process in SNNPCA is physically meaningful and convergent. Furthermore, SNNPCA inherits the parallel nature of neural network algorithms, so it is also insensitive to measurement noise. Simulation results show that SNNPCA can identify the principal modal parameters accurately using only measurement response signals. This method can be applied in embedded devices to realize online monitoring and real-time fault diagnosis.
