Adaptive operational modal identification for slow linear time-varying structures based on frozen-in coefficient method and limited memory recursive principal component analysis

Data-driven methods for operational modal parameters identification: A comparison and application

The flammable and explosive property of hydrogen is the main danger in its safe use, storage and transportation. In this paper, a novel hydrogen monitoring system is designed based on the principle of semiconductor, catalytic combustion and heat-conducting gas sensors. Also, the gas sensor will inevitably fail due to the nature of gas sensitive materials in the long-time monitoring process. To ensure the accuracy and reliability of hydrogen concentration measurement, a novel fault diagnosis and reconfiguration strategy for hydrogen sensor array based on moving window principle component analysis and extreme learning machine (MWPCA-ELM) is proposed. Firstly, online multiple faults detection is carried out by using MWPCA. Once one or multiple faults are detected, the measured values of other fault-free sensors will be used to recover the faulty data in real-time by using ELM predictor according to the relevancy among the hydrogen sensors. Secondly, the hydrogen concentration is reconfigured seamlessly and accurately based on ELM under the condition of small calibration data sample. Finally, fault diagnosis is conducted by MWPCA feature extraction coupled with ELM multi-classifier. In order to illustrate the effectiveness and feasibility of the proposed fault diagnosis and reconfiguration strategy, a hydrogen concentration monitoring experimental system was established. The average relative error (ARE) of hydrogen concentration estimation is declined from 1.18% to 0.82% compared with the traditional regression methods. Particularly, the proposed fault reconfiguration model can recover the fault data even if the concentration is changed, and the accuracy of fault diagnosis is 100% within 250 samples.

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A Fault Diagnosis and Reconfiguration Strategy for Self-Validating Hydrogen Sensor Array Based on MWPCA and ELM

Application of decoupled ARMA model to modal identification of linear time-varying system based on the ICA and assumption of “short-time linearly varying”

Deep regression adaptation networks with model-based transfer learning for dynamic load identification in the frequency domain

Operational modal analysis for slow linear time-varying structures based on moving window second order blind identification

From the principle of independent component analysis (ICA) and the uncertainty of amplitude, order, and number of source signals, this paper expounds the root reasons for modal energy uncertainty, identified order uncertainty, and modal missing in output-only modal analysis based on ICA methods. Aiming at the problem of lack of comparison and evaluation of different ICA algorithms for output-only modal analysis, this paper studies the different objective functions and optimization methods of ICA for output-only modal parameter identification. Simulation results on simply supported beam verify the effectiveness, robustness, and convergence rate of five different ICA algorithms for output-only modal parameters identification and show that maximization negentropy with quasi-Newton iterative of ICA method is more suitable for modal parameter identification.

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Comparison of Different Independent Component Analysis Algorithms for Output-Only Modal Analysis

Uncorrelated multi-sources load identification in frequency domain based on improved Tikhonov regularization method

Due to the ill-conditioned inverse characteristics of uncorrelated multi-source random dynamic load identification problem, there are large condition number and large identification errors for classic least-squares of generalization method at inherent natural frequencies. In order to avoid its illness and singularity, this multiobjective optimization inverse problem is turned into single-objective optimization forward problem by criterion function of minimization maximum relative errors of all response measuring points, and we adopt genetic algorithm to search this optimal solution then. Results of uncorrelated multi-source vibration load identification on cylindrical shell CAE simulation data set show that this new method is much better in precision and is less sensitive for measurement noise than classic least-squares of generalization method.

Uncorrelated multi-source random dynamic load identification based on minimization maximum relative errors and genetic algorithm

Most complex engineering structures are three-dimensional in practice. The process of one-dimensional extending to three-dimensional is a challenge that must be conquered by Operational Modal Analysis (OMA) methods when these methods are applied to complex engineering applications supported by scientific researches. This study put forward a new three-dimensional structure OMA method based on Second-Order Blind Identification (SOBI) and general reversion of least square. Firstly, modal coordinates decomposition of one-dimensional structural vibration response signal with SOBI. Secondly, the reasons that modal parameters identified by SOBI including energy uncertainty, order uncertainty and modal missing are explained in theory. Thirdly, the SOBI algorithm is used to decompose the response signals of displacement of a direction whose vibration response is the largest, then the other two directions are calculated by using the least square generalized inverse algorithm, and the modal parameters of three-dimensional structures are identified by the matrix assembly method. Numerical simulation results in a cylindrical shell demonstrated that this novel method is practical and effective by applied to practice in OMA of three-dimensional structures, and robustness to Gauss measurement noise disturbances.

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Jianying Wang

Papers

Operational modal analysis for slow linear time-varying structures based on moving window second order blind identification

Comparison of Different Independent Component Analysis Algorithms for Output-Only Modal Analysis

Uncorrelated multi-sources load identification in frequency domain based on improved Tikhonov regularization method

Uncorrelated multi-source random dynamic load identification based on minimization maximum relative errors and genetic algorithm

Operational modal analysis of three-dimensional structures by second-order blind identification and least square generalized inverse