This report contains a review of the technical literature concerning the detection, location, and characterization of structural damage via techniques that examine changes in measured structural vibration response. The report first categorizes the methods according to required measured data and analysis technique. The analysis categories include changes in modal frequencies, changes in measured mode shapes (and their derivatives), and changes in measured flexibility coefficients. Methods that use property (stiffness, mass, damping) matrix updating, detection of nonlinear response, and damage detection via neural networks are also summarized. The applications of the various methods to different types of engineering problems are categorized by type of structure and are summarized. The types of structures include beams, trusses, plates, shells, bridges, offshore platforms, other large civil structures, aerospace structures, and composite structures. The report describes the development of the damage-identification methods and applications and summarizes the current state-of-the-art of the technology. The critical issues for future research in the area of damage identification are also discussed.

/pdf/damage-identification-and-health-monitoring-of-structural-3s8adkph2w.pdf

Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: A literature review

Over the past 30 years detecting damage in a structure from changes in global dynamic parameters has received considerable attention from the civil, aerospace and mechanical engineering communities. The basis for this approach to damage detection is that changes in the structure's physical properties (i.e., boundary conditions, stiffness, mass and/or damping) will, in turn, alter the dynamic characteristics (i.e., resonant frequencies, modal damping and mode shapes) of the structure. Changes in properties such as the flexibility or stiffness matrices derived from measured modal properties and changes in mode shape curvature have shown promise for locating structural damage. However, to date there has not been a study reported in the technical literature that directly compares these various methods. The experimental results reported in this paper and the results of a numerical study reported in an accompanying paper attempt to fill this void in the study of damage detection methods. Five methods for damage assessment that have been reported in the technical literature are summarized and compared using experimental modal data from an undamaged and damaged bridge. For the most severe damage case investigated, all methods can accurately locate the damage. The methods show varying levels of success when applied to less severe damage cases. This paper concludes by summarizing some areas of the damage identification process that require further study.

https://iopscience.iop.org/article/10.1088/0964-1726/7/5/013/pdf

Comparative study of damage identification algorithms applied to a bridge: I. Experiment

A fault signal diagnosis technique for internal combustion engines that uses a continuous wavelet transform algorithm is presented in this paper. The use of mechanical vibration and acoustic emission signals for fault diagnosis in rotating machinery has grown significantly due to advances in the progress of digital signal processing algorithms and implementation techniques. The conventional diagnosis technology using acoustic and vibration signals already exists in the form of techniques applying the time and frequency domain of signals, and analyzing the difference of signals in the spectrum. Unfortunately, in some applications the performance is limited, such as when a smearing problem arises at various rates of engine revolution, or when the signals caused by a damaged element are buried in broadband background noise. In the present study, a continuous wavelet transform technique for the fault signal diagnosis is proposed. In the experimental work, the proposed continuous wavelet algorithm was used for fault signal diagnosis in an internal combustion engine and its cooling system. The experimental results indicated that the proposed continuous wavelet transform technique is effective in fault signal diagnosis for both experimental cases. Furthermore, a characteristic analysis and experimental comparison of the vibration signal and acoustic emission signal analysis with the proposed algorithm are also presented in this report.

Continuous wavelet transform technique for fault signal diagnosis of internal combustion engines

: This study investigated a number of different damage detection algorithms for structural health monitoring of a typical cable-stayed bridge. The Bayview Bridge, a cable-stayed bridge in Quincy, Illinois, was selected for the study. The focus was in comparing the viability of simplified techniques for practical applications. Accordingly, the numerical analysis involved development of a precise linear elastic finite element model (FEM) to simulate various structural health monitoring test scenarios with accelerometers. The Effective Independence Method was employed to locate the best distribution of the accelerometers along the length of the bridge. The simulated accelerometer data based on the FEM analysis was employed for the evaluation of the four damage identification methods investigated here. These methods included the Enhanced Coordinate Modal Assurance Criterion, Damage Index Method, Mode Shape Curvature Method, and Modal Flexibility Index Method. Some of these methods had been previously applied only to a number of specific bridges. However, the investigation here provides the relative merits and shortcomings of the damage detection methods in long-span cable-stayed bridges.

Numerical Evaluation of Vibration-Based Methods for Damage Assessment of Cable-Stayed Bridges

A statistical pattern classification method based on wavelet packet transform (WPT) is developed in this study for structural health monitoring. The core of this method is the WPT with the ability of extracting minute abnormality from vibration signals. The vibration signals of a structure excited by a pulse load are first decomposed into wavelet packet components. Signal energies of these wavelet packet components are then calculated and sorted according to their magnitudes. Those components that are small in signal energy are discarded since they are easily contaminated by measurement noise. The remaining dominant component energies are defined as a novel condition index, the wavelet packet signature (WPS). Two damage indicators are then formulated to lump the discriminate information from the extracted WPS. Thresholds for damage alarming are established using the statistical properties and the 1-sided confidence limit of the damage indicators from successive measurements. To demonstrate, an experimental study on the health monitoring of a steel cantilever I beam is performed. Four damage cases involving line cuts of different severities in the flanges at 1 cross section are introduced. Vibration signals are obtained from an accelerometer installed at the free end of the beam. Results show that the health condition of the beam can be accurately monitored by the proposed method even when the signals are highly contaminated with noise. The proposed method does not require any prior knowledge of the structure being monitored and is suitable for on-line continuous monitoring of structural health condition.

S.A. Bluni

Papers

Modified Chain-code Computer Vision Techniques For Interrogation Of Vibration Signatures For Structural Fault Detection