Modal analysis is used extensively for understanding the dynamic behavior of structures. However, a major concern for structural dynamicists is that its validity is limited to linear structures. New developments have been proposed in order to examine nonlinear systems, among which the theory based on nonlinear normal modes is indubitably the most appealing. In this paper, a different approach is adopted, and proper orthogonal decomposition is considered. The modes extracted from the decomposition may serve two purposes, namely order reduction by projecting high-dimensional data into a lower-dimensional space and feature extraction by revealing relevant but unexpected structure hidden in the data. The utility of the method for dynamic characterization and order reduction of linear and nonlinear mechanical systems is demonstrated in this study.

The Method of Proper Orthogonal Decomposition for Dynamical Characterization and Order Reduction of Mechanical Systems: An Overview

Vehicle system dynamics integration encompasses interdisciplinary challenges innovations in various aspects related to vehicle system/subsystems/components dynamic characteristics, modeling and validation, vehicle dynamics state measurement and estimation, vehicle/chassis control systems, coordination of power management and dynamics/stability control, etc [1-4]. These are becoming more critical due to the increasing concern and rapid development in electric and hybrid vehicles, which tend to exhibit different vehicle dynamics/stability and control characteristics when compared to conventional vehicles.

Mechanical systems and signal processing

BACKGROUND The tight integration of sensing, actuation, and computation that biological systems exhibit to achieve shape and appearance changes (like the cuttlefish and birds in flight), adaptive load support (like the banyan tree), or tactile sensing at very high dynamic range (such as the human skin) has long served as inspiration for engineered systems. Artificial materials with such capabilities could enable airplane wings and vehicles with the ability to adapt their aerodynamic profile or camouflage in the environment, bridges and other civil structures that could detect and repair damages, or robotic skin and prosthetics with the ability to sense touch and subtle textures. The vision for such materials has been articulated repeatedly in science and fiction (“programmable matter”) and periodically has undergone a renaissance with the advent of new enabling technology such as fast digital electronics in the 1970s and microelectromechanical systems in the 1990s. ADVANCES Recent advances in manufacturing, combined with the miniaturization of electronics that has culminated in providing the power of a desktop computer of the 1990s on the head of a pin, is enabling a new class of “robotic” materials that transcend classical composite materials in functionality. Whereas state-of-the-art composites are increasingly integrating sensors and actuators at high densities, the availability of cheap and small microprocessors will allow these materials to function autonomously. Yet, this vision requires the tight integration of material science, computer science, and other related disciplines to make fundamental advances in distributed algorithms and manufacturing processes. Advances are currently being made in individual disciplines rather than system integration, which has become increasingly possible in recent years. For example, the composite materials community has made tremendous advances in composites that integrate sensing for nondestructive evaluation, and actuation (for example, for shape-changing airfoils), as well as their manufacturing. At the same time, computer science has created an entire field concerned with distributed algorithms to collect, process, and act upon vast collections of information in the field of sensor networks. Similarly, manufacturing has been revolutionized by advances in three-dimensional (3D) printing, as well as entirely new methods for creating complex structures from unfolding or stretching of patterned 2D composites. Finally, robotics and controls have made advances in controlling robots with multiple actuators, continuum dynamics, and large numbers of distributed sensors. Only a few systems have taken advantage of these advances, however, to create materials that tightly integrate sensing, actuation, computation, and communication in a way that allows them to be mass-produced cheaply and easily. OUTLOOK Robotic materials can enable smart composites that autonomously change their shape, stiffness, or physical appearance in a fully programmable way, extending the functionality of classical “smart materials.” If mass-produced economically and available as a commodity, robotic materials have the potential to add unprecedented functionality to everyday objects and surfaces, enabling a vast array of applications ranging from more efficient aircraft and vehicles, to sensorial robotics and prosthetics, to everyday objects like clothing and furniture. Realizing this vision requires not only a new level of interdisciplinary collaboration between the engineering disciplines and the sciences, but also a new model of interdisciplinary education that captures both the disciplinary breadth of robotic materials and the depth of individual disciplines.

Materials that couple sensing, actuation, computation, and communication

Structural damage diagnosis under varying environmental conditions—Part I: A linear analysis

This paper reviews the development of energy harvesting for low-power embedded structural health monitoring (SHM) sensing systems. A statistical pattern recognition paradigm for SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to the data acquisition portion of this paradigm. Next, various existing and emerging sensing modalities used for SHM and their respective power requirements are summarized followed by a discussion of SHM sensor network paradigms, power requirements for these networks, and power optimization strategies. Various approaches to energy harvesting and energy storage are discussed and limitations associated with the current technology are addressed. The paper concludes by defining some future research directions that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes. Finally, it is noted that many of the technologies discussed herein are applicable to powering any type of low-power embedded sensing system regardless of the application.

/pdf/energy-harvesting-for-structural-health-monitoring-sensor-40xb7fid8o.pdf

Energy Harvesting for Structural Health Monitoring Sensor Networks

For a reliable on-line vibration monitoring of structures, it is necessary to have accurate sensor information. However, sensors may sometimes be faulty or may even become unavailable due to failure or maintenance activities. The problem of sensor validation is therefore a critical part of structural health monitoring. The objective of the present study is to present a procedure based on principal component analysis which is able to perform detection, isolation and reconstruction of a faulty sensor. Its efficiency is assessed using an experimental application.

https://iopscience.iop.org/article/10.1088/0964-1726/14/1/004/pdf

Sensor validation using principal component analysis

This paper focuses on applying statistical process control techniques, based on principal component analysis, to vibration-based damage diagnosis. It is well known that localized structural damages with relative small amplitude do not affect much the global modal response of the structure, at least at low frequencies. Nevertheless, it can be expected that the local dynamic behavior of a damaged structural subcomponent is significantly affected. Assuming that each structural subcomponent is monitored, local structural damage, with relative small amplitude, will only affect a particular sensor without affecting significantly the response of the others. By applying a principal component analysis on the sensor time responses, it is possible to see that any change of one particular sensor will affect the subspace spanned by the complete sensor response set. The subspace corresponding to the damaged structure can then be compared with the subspace of an initial state in order to diagnose possible damage. The pr...

Principal Component Analysis of a Piezosensor Array for Damage Localization

This paper presents an application of statistical process control techniques for damage diagnosis using vibration measurements. A Kalman model is constructed by performing a stochastic subspace identification to fit the measured response histories of the undamaged (reference) structure. It will not be able to reproduce the newly measured responses when damage occurs. The residual error of the prediction by the identified model with respect to the actual measurement of signals is defined as a damage-sensitive feature. The outlier statistics provides a quantitative indicator of damage. The advantage of the method is that model extraction is performed by using only the reference data and that no further modal identification is needed. On-line health monitoring of structures is therefore easily realized. When the structure consists of the assembly of several sub-structures, for which the dynamic interaction is weak, the damage may be located as the errors attain the maximum at the sensors instrumented in the ...

Structural damage diagnosis by Kalman model based on stochastic subspace identification

Sensor Validation for On-line Vibration Monitoring

Structural damage diagnosis under changing environmental conditions

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