Hoon Sohn 교수

Stated in its most basic form, the objective of structural health monitoring is to ascertain if damage is present or not based on measured dynamic or static characteristics of a system to be monitored. In reality, structures are subject to changing environmental and operational conditions that affect measured signals, and these ambient variations of the system can often mask subtle changes in the system's vibration signal caused by damage. Data normalization is a procedure to normalize datasets,

A novel time series analysis is presented to locate damage sources in a mechanical system, which is running in various operational environments. The source of damage is located by solely analyzing the acceleration time histories recorded from a structure of interest. First, a data normalization procedure is proposed. This procedure selects a reference signal that is `closest' to a newly obtained signal from an ensemble of signals recorded when the structure is undamaged. Second, a two-stage pred

This paper casts structural health monitoring in the context of a statistical pattern recognition paradigm. Two pattern recognition techniques based on time series analysis are applied to fiber optic strain gauge data obtained from two different structural conditions of a surface-effect fast patrol boat. The first technique is based on a two-stage time series analysis combining Auto-Regressive (AR) and Auto-Regressive with eXogenous inputs (ARX) prediction models. The second technique employs an

This paper poses the process of structural health monitoring in the context of a statistical pattern recognition paradigm. This paper particularly focuses on applying a statistical process control (SPC) technique known as an “X-bar control chart” to vibration-based damage diagnosis. A control chart provides a statistical framework for monitoring future measurements and for identifying new data that are inconsistent with past data. First, an autoregressive (AR) model is fit to the measured time h

Damage detection techniques have been proposed to exploit changes in modal parameters and to identify the extent and location of damage in large structures. Most of such techniques, however, generally neglect the environmental effects on modal parameters. Such environmental effects include changes in loads, boundary conditions, temperature, and humidity. In fact, the changes due to environmental effects can often mask more subtle structural changes caused by damage. This paper examines a linear

In this paper a signal processing technique is developed to detect delamination on composite structures. In particular, a wavelet-based signal processing technique is developed and combined with an active sensing system to produce a near-real-time, online monitoring system for composite structures. A layer of piezoelectric patches is used to generate an input signal with a specific wavelet waveform and to measure response signals. Then, the response signals are processed by a wavelet transform t

Stated in its most basic form, the objective of damage diagnosis is to ascertain simply if damage is present or not based on measured dynamic characteristics of a system to be monitored. In reality, structures are subject to changing environmental and operational conditions that affect measured signals, and environmental and operational variations of the system can often mask subtle changes in the system’s vibration signal caused by damage. In this paper, a unique combination of time series anal

A Bayesian probabilistic approach is presented for the damage detection of multistorey frame structures. In this paper, a Bayesian probabilistic approach is applied to identify multiple damage locations using estimated modal parameters when (1) the measurement data are potentially corrupted with noise, (2) only a small number of degrees of freedom are measured, and (3) a few fundamental modes are estimated. To reduce the potentially intensive computational cost of the proposed method, a branch-a

This paper presents an automated crack visualization technique using ultrasonic wavefield images obtained by a complete noncontact laser scanning system. First, the complete noncontact laser scanning system is built by integrating and synchronizing a Q-switched Nd:YAG laser for ultrasonic generation, a laser Doppler vibrometer for ultrasonic measurement and galvanometers for scanning. Then, four different laser scanning schemes are compared to find the most effective and practical ultrasonic sca