Parallel principal components algorithm for OMA following Sanger neural network

• Published in: International journal of applied electromagnetics and mechanics Vol. 59; no. 4; pp. 1401 - 1412
• Main Authors: Wang, Cheng, Huang, Haiyang, Zhang, Tianshu, Chen, Yewang, Zhang, Yiwen, Cheng, Jianwei
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.04.2019; Sage Publications Ltd
• Subjects: Algorithms; Computer simulation; Convergence; Electronic devices; Embedded systems; Fault diagnosis; Modal analysis; Modal identification; Neural networks; Neurons; Noise measurement; Noise sensitivity; Parameter identification; Principal components analysis; Sensitivity analysis; Singularities; Sanger neural network; generalized Hebbian rule; parallel; measurement noise; principal component analysis; Operational modal analysis
• ISSN: 1383-5416; 1875-8800
• DOI: 10.3233/JAE-171011

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.