Amanda C. Rutherford

Piezoelectric Active Sensor Self-Diagnostics using Electrical Admittance Measurements

This paper presents a piezoelectric sensor self-diagnostic procedure that performs in situ monitoring of the operational status of piezoelectric materials used for sensors and actuators in structural health monitoring (SHM) applications. The sensor/actuator self-diagnostic procedure, where the sensors/actuators are confirmed to be functioning properly during operation, is a critical component to successfully complete the SHM process with large numbers of active sensors typically installed in a structure. The premise of this procedure is to track the changes in the capacitive value of piezoelectric materials resulting from the degradation of the mechanical/electrical properties and its attachment to a host structure, which is manifested in the imaginary part of the measured electrical admittances. This paper concludes with an experimental example to demonstrate the feasibility of the proposed procedure.

High-Frequency Response Functions for Composite Plate Monitoring with Ultrasonic Validation

In this study, frequency response functions (FRF) measured by piezoelectric Macro-Fiber Composite (MFC) actuators/sensors are used to detect subsurface delamination in a composite plate. The plate is impacted to seed damage in the form of ply delamination. Then, the MFCbased active-sensing system exerts an excitation into the plate, and measures the subsequent responses. Traditional piezoceramic materials are also mounted in comparable locations on the plate to compare their performances. FRF and damage indicator features are derived from the measured signals and used to assess the condition of the plate. Validation of the delamination is completed using an ultrasonic C-scan method. The effective area of observed damage is well correlated to the damage indicator feature.

Use of Response Surface Metamodels for Identification of Stiffness and Damping Coefficients in a Simple Dynamic System

Metamodels have been used with success in many areas of engineering for decades but only recently in the field of structural dynamics. A metamodel is a fast running surrogate that is typically used to aid an analyst or test engineer in the fast and efficient exploration of the design space. Response surface metamodels are used in this work to perform parameter identification of a simple five degree of freedom system, motivated by their low training requirements and ease of use. In structural dynamics applications, response surface metamodels have been utilized in a forward sense, for activities such as sensitivity analysis or uncertainty quantification. In this study a polynomial response surface model is developed, relating system parameters to measurable output features. Once this relationship is established, the response surface is used in an inverse sense to identify system parameters from measured output features. A design of experiments is utilized to choose points, representing a fraction of the full design space of interest, for fitting the response surface metamodel. Two parameters commonly used to characterize damage in a structural system, stiffness and damping, are identified. First changes are identified and located with success in a linear 5DOF system. Then parameter identification is attempted with a nonlinear 5DOF system and limited success is achieved. This work will demonstrate that use of response surface metamodels in an inverse sense shows promise for use in system parameter identification for both linear and weakly nonlinear systems and that the method has potential for use in damage identification applications.

Structural Health Monitoring Using Macro-Fiber Composites and Impedance Methods

A current technique that has been the subject of a great deal of study in the structural health monitoring community is the impedance method, which uses high frequency responses to monitor a local area of a structure for changes in structural impedance that indicate damage. To date, piezoceramic sensors, whose electrical impedance is directly coupled with the structure’s mechanical impedance, have been used as both sensors and actuators for impedance measurements and this practice is fairly well developed. However because these sensors are ceramic, they are brittle, making them vulnerable to accidental breakage. They also conform poorly to curved surfaces. In this study macro-fiber composite (MFC) patches, which are flexible in nature, are examined for feasibility of use with the impedance method. Two test structures with both MFC patches and PZTs are considered. Both traditional linear features and newer nonlinear features are used for structural health monitoring. High frequency responses of the MFC patches are examined and they yield damage identification results that are comparable to piezoelectric impedance signals and indicate that MFCs are suitable for impedance sensing and other high-frequency structural health monitoring.

DAMAGE IDENTIFICATION USING IMPEDANCE METHODS COUPLED WITH STATISTICAL CLASSIFIERS

This paper presents the use of statistically rigorous algorithms combined with active-sensing impedance methods for damage identification in engineering systems. In particular, we propose to use statistical pattern recognition methods to address damage classification and data mining issues associated with the examination of large numbers of impedance signals for health monitoring applications. The impedance-based structural health monitoring technique, which utilizes electromechanical coupling properties of piezoelectric materials, has shown feasibility for use in a variety of damage identification applications. Relying on high frequency local excitations (typically>30 kHz), this technique is very sensitive to minor changes in structural integrity in the near field of piezoelectric sensors. In this study, in order to diagnosis damage with levels of statistical confidence, the impedance-based monitoring is cast in the context of an outlier detection framework. A modified autoregressive model with exogenous inputs (ARX) in the frequency domain is developed. The damage sensitive feature is then computed by differentiating the measured impedance and the output of the ARX model. Furthermore, because of the non-Gaussian nature of the feature distribution tails, extreme value statistics (EVS) are employed to develop a robust damage classifier. By incorporating EVS, we establish a rigorous impedance-based health monitoring algorithm, which is able to provide structural systems with self-contained and selfdiagnostic components. This paper concludes with a numerical example on a 5 degree-of-freedom system and an experimental investigation on a multi-story building model to demonstrate the performance of the proposed concept.

Nonlinear feature identification of impedance-based structural health monitoring

The impedance-based structural health monitoring technique, which utilizes electromechanical coupling properties of piezoelectric materials, has shown feasibility for use in a variety of structural health monitoring applications. Relying on high frequency local excitations (typically>20 kHz), this technique is very sensitive to minor changes in structural integrity in the near field of piezoelectric sensors. Several damage sensitive features have been identified and used coupled with the impedance methods. Most of these methods are, however, limited to linearity assumptions of a structure. This paper presents the use of experimentally identified nonlinear features, combined with impedance methods, for structural health monitoring. Their applicability to for damage detection in various frequency ranges is demonstrated using actual impedance signals measured from a portal frame structure. The performance of the nonlinear feature is compared with those of conventional impedance methods. This paper reinforces the utility of nonlinear features in structural health monitoring and suggests that their varying sensitivity in different frequency ranges may be leveraged for certain applications.

The Use of Frequency Response Functions for Composite Plate Monitoring With Ultrasonic Validations

In this study, frequency response functions (FRF) measured by piezoelectric Macro-Fiber Composites (MFC) are used to detect invisible delamination in a composite plate. The plate is impacted to seed damage in the form of fiber delamination. Then, the active-sensing system exerts an excitation into the plate, and measures the subsequent responses. FRF and damage indicator features are derived from the measured signals and used to assess the condition of the plate. Validation of the delamination is completed using an ultrasonic scan method. The effective area of observed damage is well correlated to the damage indicator feature.Copyright