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LA-UR-02-2095
A Review of Structural Health Monitoring Literature 1996 – 2001
Hoon Sohn1, Charles R. Farrar1 Francois Hemez1 and Jerry Czarnecki2
1Eng. Science and Applications Div., Los Alamos National Laboratory, Los Alamos, NM 87545, USA
2Department of Civil Engineering, Massachutts Institute of Tech., Cambridge, MA 02139, USA
小学数学教师论文ABSTRACT
Staff members at Los Alamos National Laboratory (LANL) produced a summary of the structural health monitoring literature in 1995. This prentation will summarize the outcome of
an updated review covering the years 1996 - 2001. The updated review follows the LANL statistical pattern recognition paradigm for SHM, which address four topics: 1. Operational Evaluation; 2. Data Acquisition and Cleansing; 3. Feature Extraction; and 4. Statistical Modeling
for Feature Discrimination. The literature has been reviewed bad on how a particular study address the four topics. A significant obrvation from this review is that although there are many more SHM studies being reported, the investigators, in general, have not yet fully embraced the well-developed tools from statistical pattern recognition. As such, the discrimination procedures employed are often lacking the appropriate rigor necessary for this technology to evolve beyond dem
onstration problems carried out in laboratory tting.
1. INTRODUCTION
This paper provides a synopsis of a review [1] that will summarize structural health monitoring studies that have appeared in the technical literature between 1996 and 2001. The primary purpo of this review is to update a previous literature review [2, 3] on the same subject. As with the previous documents, this summary will not address structural health monitoring applied to rotating machinery or local nondestructive testing techniques. Instead, this review, as
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well as the previous one, focus on global structural health monitoring.
This review begins by defining structural health monitoring process in terms of a statistical pattern recognition paradigm. The u of this paradigm in the literature review reprents a significant change in the way this review is organized compared to the previous one. The critical issues for this technology that were identified at the completion of the previous review are then
SOHN, FARRAR, HEMEZ and CZARNECKI 2 briefly summarized. In the first part of review, the literature is summarized in terms of how each stu
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dy fits into the statistical pattern recognition paradigm. In the cond portion, the literature is summarized with respect to the various applications that have been reported. This review concludes by attempting to summarize progress that has been made with regard to critical issues identified in the previous review and identifies new issues as they become apparent from the new literature.
1.1 Summary of the Previous Review
Doebling, et al., [2,3] provides one of the most comprehensive reviews of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration respon. Issues that were identified include the dependence of many methods on prior analytical models for the detection and location of damage. Also, almost all of the damage-identification methods reviewed on some type of a linear structural model. The number and location of nsors was another important issue that was not addresd to any significant extent in the previously reviewed literature. An issue that was a point of controversy among many rearchers was the general level of nsitivity that modal parameters have to small flaws in a structure. A related issue was the discernment of changes in the modal properties resulting from damage from tho caud by natural variations in the measurements. The literature was also found to have scarce instances of studies where different health-monitoring procedures were compa
red directly through application to common data ts. Additionally, rearch appeared not to be focud more on testing of real structures in their operating environment, but rather on laboratory tests of simple structural systems in controlled environments.
1.2 The Structural Health Monitoring Process
The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as Structural Health Monitoring (SHM).
The authors believe that the SHM problem is fundamentally one of statistical pattern recognition. Therefore, the damage detection studies reviewed herein are summarized in the context of a statistical pattern recognition paradigm [4]. This paradigm can be described as a four-part process: (1) Operational Evaluation, (2) Data Acquisition, Fusion and Cleansing, (3) Feature Extraction and Information Condensation, and (4) Statistical Model Development for Feature Discrimination.
2. OPERATIONAL EVALUATION
基辛格名言Operational evaluation answers four questions regarding the implementation of a structural health monitoring system: 1)How is damage defined for the system being monitored? 2) What are the conditions, both operational and environmental, under which the system to be monitored
SOHN, FARRAR, HEMEZ and CZARNECKI 3 functions? 3) What are limitations on acquiring data in the operational environment? 4)What are teconomic and/or life safety motives for performing the monitoring?
Cawley [5] not only specifies the type of damage, but also he quantifies the extent of detectable damage in terms of the pipe diameter and wall thickness. Staszewski, et al. [6] demonstrate that temperature and ambient vibrations can affect the performance of piezoelectric nsors employed in composite plate tests. Bartelds [7] provides an example of a study where economic and life safety issues have been addresd. He states that the direct costs of carrying out preventive inspections and the indirect costs associated with interrupted rvice provide a strong stimulus for developing a SHM system for aircraft.
In summary, few of the studies examined in this review address the operational evaluation portion of the SHM paradigm becau the studies are focud on laboratory tests. For such tests the damage is prescribed and there is little or no operational or environmental variability. The studies are done as part of rearch efforts so there is little attention paid to economical and/or life safety justifications for the monitoring system. However, the authors feel that the issues must be addresd if SHM is to make the transition from a rearch topic to uful systems deployed in the fi
eld.
3. DATA ACQUISITION, FUSION AND CLEANSING
The data acquisition portion of the structural health monitoring process involves lecting the types of nsors to be ud, the locations where the nsors should be placed, the number of nsors to be ud, and the data acquisition/storage/transmittal hardware. Another consideration is how often the data should be collected. There are a large number of studies reporting the development of new nsors and nsing systems for SHM applications. In particular there are significantly more studies on the u of fiber optic nsors (e.g. [8]), MEMS nsors (e.g. [9]), and wireless data acquisitions system (e.g. [10]) appearing in this SHM literature relative to the previous review.
Becau data can be measured under varying conditions, the ability to normalize the data becomes very important to the SHM process. One of the most common procedures is to normalize the measured respons by the measured inputs. Sohn, et al. [11] summarizes a procedure for such normalization when direct measures of the varying input are not available. When environmental or operating condition variability is an issue, the need can ari to normalize the data in some temporal fashion to facilitate the comparison of data measured at similar times of an environmental or operatio
nal cycle. As an example, Doebling and Farrar [12] measured the temperature differential across the deck of a bridge at 2 hr increments during a 24 hr cycle and correlate the measurements with the change in the bridge’s natural frequencies. The purpo of data fusion is to integrate data from a multitude of nsors to make a more confident damage detection decision than is possible with any one nsor alone. In many cas data fusion is performed in an unsophisticated manner such as when one examines relative
SOHN, FARRAR, HEMEZ and CZARNECKI 4 information between various nsors to obtain mode shapes. At other times complex analys of information from nsor arrays such as tho provided by artificial neural networks [13], are ud in the data fusion process. Data cleansing is the process of lectively choosing data to accept for, or reject from, the feature lection process. Filtering and decimation are two common data cleansing procedures applied to data acquired during dynamic tests. The techniques are ud extensively in the reviewed literature although they are not generally identified by the term “data cleansing”.
4. FEATURE EXTRACTION AND INFORMATION COMDENSATION
The area of the SHM that receives the most attention in the technical literature, both in the current a
nd previous review, is feature extraction. Feature extraction is the process of the identifying damage-nsitive properties, derived from the measured system respon, which allows one to distinguish between the undamaged and damaged structure. In the current and previous reviews linear modal properties (resonant frequencies, mode shapes, or properties derived from mode shapes such a flexibility coefficients) are the most common features ud for damage detection
The current review shows that more investigators are using features that are associated with the systems transition from a predominantly linear, time-invariant system to a system exhibiting nonlinear and time varying respon as a result of damage (e.g. [14, 15]).
村居教学设计The implementation and diagnostic measurement technologies needed to perform SHM typically produce a large amount of data. Almost all feature extraction procedures inherently perform some form of data compression. Data compression into feature vectors of small dimension is necessary if accurate estimates of the feature’s statistical distribution are to be obtained. As an example, the u of residual errors between auto-regressive model predictions and actual measured time histories reprents a one-dimensional feature vector that has been ud for damage detection [11]. Note that the data fusion process previously discusd can also be thought of a form of information condensation.
5. STATISTICAL MODEL DEVELOPMENT
The portion of the structural health monitoring process that has received the least attention in the current and previous review is the development of statistical models to enhance the SHM process. Almost none of the hundreds of studies summarized in [2, 3] make u of any statistical methods to asss if the changes in the lected features ud to identify damaged systems are statistically significant. The algorithms ud in statistical model development usually fall into three categories. When data are available from both the undamaged and damaged structure, the statistical pattern recognition algorithms fall into the general classification referred to as supervid learning. Group classification and regression analysis are supervid learning
