This paper provides an overview of methods to detect, locate, and characterize damage in structural and mechanical systems by examining changes in measured vibration response. Research in vibration-based damage identification has been rapidly expanding over the last few years. The basic idea behind this technology is that modal parameters (notably frequencies, mode shapes, and modal damping) are functions of the physical properties of the structure (mass, damping, and stiffness). Therefore, changes in the physical properties will cause detectable changes in the modal properties. The motivation for the development of this technology is presented. The methods are categorized according to various criteria such as the level of damage detection provided, model-based versus non-model-based methods, and linear versus nonlinear methods. The methods are also described in general terms including difficulties associated with their implementation and their fidelity. Past, current, and future-planned applications of this technology to actual engineering systems are summarized. The paper concludes with a discussion of critical issues for future research in the area of vibration-based damage identification.

A summary review of vibration-based damage identification methods

We consider the problem of choosing a set of k sensor measurements, from a set of m possible or potential sensor measurements, that minimizes the error in estimating some parameters. Solving this problem by evaluating the performance for each of the (m k) possible choices of sensor measurements is not practical unless m and k are small. In this paper, we describe a heuristic, based on convex optimization, for approximately solving this problem. Our heuristic gives a subset selection as well as a bound on the best performance that can be achieved by any selection of k sensor measurements. There is no guarantee that the gap between the performance of the chosen subset and the performance bound is always small; but numerical experiments suggest that the gap is small in many cases. Our heuristic method requires on the order of m 3 operations; for m= 1000 possible sensors, we can carry out sensor selection in a few seconds on a 2-GHz personal computer.

/pdf/sensor-selection-via-convex-optimization-2nryt3q469.pdf

Sensor Selection via Convex Optimization

This article reviews the development of the original modal assurance criterion (MAC) together with other related assurance criteria that have been proposed over the last twenty years. Some of the other assurance criteria that will be discussed include the coordinate modal assurance criterion (COMAC), the frequency response assurance criterion (FRAC), coordinate orthogonality check (CORTHOG), frequency scaled modal assurance criterion (FMAC), partial modal assurance criterion (PMAC), scaled modal assurance criterion (SMAC), and modal assurance criterion using reciprocal modal vectors (MACRV). In particular, the thought process that relates the original MAC development to ordinary coherence and to orthogonality computations will be explained. Several uses of MAC that may not be obvious to the casual observer (modal parameter estimation consistency diagrams and model updating are two examples) will be identified. The common problems with the implementation and use of modal assurance criterion computations will also be identified. The development of the modal assurance criterion 1-2 over twenty years ago has led to a number of similar assurance criteria used in the area of experimental and analytical structural dynamics. It is important to recognize the mathematical similarity of these varied criteria in order to be certain that conclusions be correctly drawn from what is essentially a squared, linear regression correlation coefficient. The modal assurance criterion is a statistical indicator, just like ordinary coherence, which can be very powerful when used correctly but very misleading when used incorrectly. This article will first review the historical development of the modal assurance criterion. Other similar assurance criteria will then be identified although the list is not intended to be comprehensive. Typical uses of the modal assurance criterion will be discussed and finally, typical abuses will be identified.

The modal assurance criterion–twenty years of use and abuse

This book focuses on structural health monitoring in the context of machine learning. The authors review the technical literature and include case studies. Chapters include: operational evaluation, sensing and data acquisition, introduction to probability and statistics, machine learning and statistical pattern recognition, and data prognosis.

Structural Health Monitoring: A Machine Learning Perspective

A method is presented for the selection of a set of sensor locations from a larger candidate set for the purpose of on-orbit identification and correlation of Large Space Structures. The method ranks the candidate sensor locations according to their contribution to the linear independence of the target modal partitions. In an iterative maner, the locations which do not contribute significantly are removed. The final sensor configuration tends to maximize determinant of the corresponding Fisher Information Matrix.

Sensor placement for on-orbit modal identification and correlation of large space structures

Sensor Placement for On-Orbit Modal Identification and Correlation of Large Space Structures

The selection and reproduction schemes of the genetic algorithm are modified, and a new operator called forced mutation is introduced. These changes are shown to improve the convergence of the algorithm and to lead to near-optimal sensor locations. Two practical examples are investigated: sensor placement for an early version of the space station and an individual space station photovoltaic array

Sensor placement for on-orbit modal identification via a genetic algorithm

http://silver.neep.wisc.edu/~kammer/triax.pdf

Optimal placement of triaxial accelerometers for modal vibration tests

Proper pretest planning is a vital component of any successful vibration test. An extremely important part of the pretest exercise is the placement of sensors, usually in the form of accelerometers. The accelerometers must be placed such that all of the important dynamic information is obtained during the course of the test. The resulting sensor configuration must be optimal in some sense such that test resources are conserved. The state-of-the-practice is to select individual sensor location/directions from a candidate set based upon one of several available criteria. Triaxial accelerometers are then placed at the corresponding locations. In general, this results in the non-optimal placement of many of the accelerometers. This paper presents a new technique, based upon Effective Independence, that places triaxial accelerometers as single units in an optimal fashion. The technique is applied and compared with standard approaches using the X-33 vehicle.

Daniel C. Kammer

Papers

Sensor placement for on-orbit modal identification and correlation of large space structures

Sensor Placement for On-Orbit Modal Identification and Correlation of Large Space Structures

Sensor placement for on-orbit modal identification via a genetic algorithm

Optimal placement of triaxial accelerometers for modal vibration tests

Optimal placement of triaxial accelerometers for modal vibration tests