Frequency response based damage detection using principal component analysis
ABSTRACT In this paper we explore structural damage detection using frequency response signals and principal component analysis. While frequency responses are easy to measure especially in online damage detection applications, most of the associated detection methods are deterministic in nature and cannot deal with uncertainties and noise which are inevitable under practical situations. To tackle this issue and to develop a robust damage detection protocol, here we develop a feature extraction/de-noising methodology based on principal component analysis (PCA). The basic idea is to first establish a feature space of the intact structure response by using multiple measurements. Abnormal signature that is different from the baseline signature can then be identified and magnified after signal reconstruction using the intact structure features. Essentially, the directionality between an inspected signal and the baseline signal in the feature space is used as index of damage occurrence. A series of numerical analyses are performed to characterize the detection system sensitivity and robustness.
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ABSTRACT: Multivariate Statistical Process Control (MSPC) tools have been developed for monitoring a Lam 9600 TCP Metal Etcher at Texas Instruments. These tools are used to determine if the etch process is operating normally or if a system fault has occurred. Application of these methods is complicated because the etch process data exhibits a large amount of normal systematic variation. Variations due to faults of process concern can be relatively minor in comparison. The Lam 9600 used in this study is equipped with several sensor systems including engineering variables ( e.g. pressure, gas flow rates and power), spatially resolved Optical Emission Spectroscopy (OES) of the plasma and a Radio Frequency Monitoring (RFM) system to monitor the power and phase relationships of the plasma generator. A variety of analysis methods and data preprocessing techniques have been tested for their sensitivity to specific system faults. These methods have been applied to data from each of the sensor systems separately and in combination. The performance of the methods on a set of benchmark fault detection problems will be presented and the strengths and weaknesses of the methods will be discussed, along with the relative advantages of each of the sensor systems.
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ABSTRACT: Cost-effective and reliable damage detection is critical for the utilization of composite materials. This paper presents part of an experimental and analytical survey of candidate methods for the in situ detection of damage in composite materials. The experimental results are presented for the application of modal analysis techniques applied to graphite/epoxy specimens containing representative damage modes. Changes in natural frequencies and modes were found using a laser vibrometer, and 2-D finite element models were created for comparison with the experimental results. The models accurately predicted the response of the specimens at low frequencies, but coalescence of higher frequency modes makes mode-dependant damage detection difficult for structural applications. The frequency response method was found to be reliable for detecting even small amounts of damage in a simple composite structure, however the potentially important information about damage type, size, location and orientation were lost using this method since several combinations of these variables can yield identical response signatures.Composites Part B: Engineering. 01/2002;