Janette J. Meyer

Reliability and accuracy of helmet-mounted and head-mounted devices used to measure head accelerations:

The attention given to brain injury has grown in recent years as its effects have become better understood. A desire to investigate the causal agents of head trauma in athletes has led to the development and use of several devices that track head impacts. In order to determine which devices best measure these impacts, a Hybrid III headform was used to quantify the accuracy for translational and angular accelerations. Testing was performed by mounting each device into the helmet as instructed by its manufacturer, fitting the helmet on the headform, and impacting the helmet using an impulse hammer. The root mean square error for the peak translational acceleration varied with location. The worst root mean square error for a head-mounted device was 74.7% while the worst for a helmet-mounted device was 298%. Head-mounted devices consistently outperformed those mounted in helmets, suggesting that future sensor designs should avoid attachment to the helmet. Deployment to a high school football team affirmed differences between two of the device models, but strongly indicated that head-mounted systems require further development to account for variation between individuals, the relative motion of the skin, and helmet–sensor interactions. Future work needs to account for these issues, refine the algorithms used to estimate the translational and angular accelerations, and examine technologies that better locate the source of the impact.

Crack detection technique for operating wind turbine blades using Vibro-Acoustic Modulation

This article presents a new technique for identifying cracks in wind turbine blades undergoing operational loads using the Vibro-Acoustic Modulation technique. Vibro-Acoustic Modulation utilizes a low-frequency pumping excitation signal in conjunction with a high-frequency probing excitation signal to create the modulation that is used to identify cracks. Wind turbines provide the ideal conditions in which Vibro-Acoustic Modulation can be utilized because wind turbines experience large low-frequency structural vibrations during operation which can serve as the low-frequency pumping excitation signal. In this article, the theory for the vibro-acoustic technique is described, and the proposed crack detection technique is demonstrated with Vibro-Acoustic Modulation experiments performed on a small Whisper 100 wind turbine in operation. The experimental results are also compared with two other conventional vibro-acoustic techniques in order to validate the new technique. Finally, a computational study is demonstrated for choosing a proper probing signal with a finite element model of the cracked blade to maximize the sensitivity of the technique for detecting cracks.

Assessing Stability and Predicting Power Generation of Electromagnetic Vibration Energy Harvesters Using Bridge Vibration Data

This paper presents the use of the power harvesting ratio (PHR) approach for evaluating the power harvesting capabilities of an electromagnetic vibration energy harvester. This is done for different electrical loads and measured bridge vibration data displaying multiple frequency components. Bridge vibration data are collected and characterized. The modes of the bridge are determined using a model sledge hammer, and the response of the bridge to a single vehicle is measured. Analysis of the data reveals that several of the modes contribute toward a response with multiple non-negligible frequency components. Measured bridge time-series data are then replayed on an experimental setup with an electromagnetic vibration energy harvester. Six electrical loads are implemented on the experimental platform: four passive loads and two active loads. The PHR approach is used to predict the average power from each load. Experimentally measured average power is within 6% of the predicted average power. The PHR approach is also used to successfully predict harvester instability for the active load dictated by the maximum power transfer theorem and validated experimentally. This paper demonstrates the utility of the PHR approach in evaluating harvester stability and performance for multifrequency excitations and sophisticated electrical loads, including active loads.

Data Acquisition Protocol for Determining Embedded Sensitivity Functions

The effectiveness of many structural health monitoring techniques depends on the placement of sensors and the location of input forces. Algorithms for determining optimal sensor and forcing locations typically require data, either simulated or measured, from the damaged structure. Embedded sensitivity functions provide an approach for determining the best available sensor location to detect damage with only data from the healthy structure. In this video and manuscript, the data acquisition procedure and best practices for determining the embedded sensitivity functions of a structure is presented. The frequency response functions used in the calculation of the embedded sensitivity functions are acquired using modal impact testing. Data is acquired and representative results are shown for a residential scale wind turbine blade. Strategies for evaluating the quality of the data being acquired are provided during the demonstration of the data acquisition process.

Dynamic Data Driven Approach for Modeling Human Error

Abstract Mitigating human errors is a priority in the designs of complex systems, especially through the use of body area networks. This paper describes early developments of a dynamic data driven platform to predict operator error and trigger appropriate intervention before the error happens. Using a two-stage process, data was collected using several sensors (e.g., electroencephalography, pupil dilation measures, and skin conductance) during an established protocol - the Stroop test. The experimental design began with a relaxation period, 40 questions (congruent, then incongruent) without a timer, a rest period followed by another two rounds of questions, but under increased time pressure. Measures such as workload and engagement showed responses consistent with what is expected for Stroop tests. Dynamic system analysis methods were then used to analyze the raw data using principal components analysis and the least squares complex exponential method. The results show that the algorithms have the potential to capture mental states in a mathematical fashion, thus enabling the possibility of prediction.

Application of SHM Pattern Recognition to Assess Decision Making of Humans in the Loop

Structural health monitoring (SHM) techniques have traditionally been applied to mechanical, aerospace, and civil structures to identify loading and damage patterns. However, human operators in the loop play an important role in the operational performance of aircraft and other structural systems. The increased availability of sensors such as EEG, skin conductance, and eye-tracking systems are creating an opportunity to develop SHM techniques for assessing neuro-physiological factors that influence human decision-making. The parallels between the structural dynamic response of a system to an excitation source and the response of a human to the presentation of a scenario suggests that SHM algorithms can be used to interpret neurophysiological signals. As in traditional SHM, where the system’s dynamic response is measured to characterize the system’s state of health, the measured response of a human during decision-making can capture information about the human’s mental state, including levels of fatigue, engagement, workload, and other human factors. The ability to monitor the human’s mental state in real-time could also enable predictions of human susceptibility to poor decision-making and to trigger an appropriate intervention to prevent human errors. In this work, EEG, eye-tracking, and skin conductance data are acquired from multiple subjects while performing the Stroop test, a standard test designed to induce errors, under varying degrees of time pressure. Time pressure is induced by progressively reducing the time-to-answer allowed for each set of questions. The data is then analyzed using pattern recognition techniques including principal component analysis and the least squares complex exponential (LSCE) parameter estimation algorithm. Results from the principal component analysis identify the modes which dominate the response during decision-making. These modes are compared to the modes identified while the subject is at rest. Next, LSCE is applied to identify model parameters that can be used to perform a one-step-ahead prediction of the neurophysiological variables. The LSCE approach allows data from the different sensor types to be analyzed simultaneously. Results show that model error is reduced as the time pressure is increased. doi: 10.12783/SHM2015/145

Embedded Sensitivity Functions for Improving the Effectiveness of Vibro-Acoustic Modulation and Damage Detection on Wind Turbine Blades

In structural health monitoring, it is desirable to select sensor locations in order to minimize the number of sensors required for and the cost associated with an on-board monitoring system. When using a frequency response-based structural health monitoring technique, data measured at sensor locations which exhibit the greatest change in frequency response function (FRF) due to damage are expected to maximize the effectiveness of the chosen technique. In this work, an embedded sensitivity function is presented which identifies the sensor locations at which the maximum differences in FRFs due to damage at a known location will be observed. The formulation of the embedded sensitivity function is based on FRFs measured on a healthy structure in the frequency range in which the damage detection technique will be applied. The effectiveness of the embedded sensitivity functions in predicting the most effective sensor locations is demonstrated by applying a vibro-acoustic modulation (VAM) damage detection method to a residential-scale wind turbine blade. First, data from the healthy blade is acquired and the embedded sensitivity functions are calculated. Then, the blade is damaged and the VAM method is applied using several sensor locations. The data acquired using sensor locations identified by the embedded sensitivity functions as being most effective are shown to most clearly identify the damage on the blade.Copyright

Identification of Nonlinear Behavior in a Composite Structure With Core-Crushing Damage

Many damage detection methods that are applied to composite structures rely on nonlinear features in the dynamic response of the structure to identify the presence of defects. Presently, there is not a complete understanding of the physical mechanisms that cause the nonlinear behavior of a damaged composite structure. Correlating specific types of damage mechanisms to the resulting nonlinear response characteristics they cause would allow the detection methods to classify the type of damage that is present in the structure. In this work, a drop tower was used to impact an aluminum honeycomb sandwich panel in order to induce core-crushing. The response of the damaged panel to sinusoidal excitations of various amplitudes at resonant, super-, and sub-harmonic frequencies was then measured. The amplitudes of these measured responses and the corresponding restoring force curves were then compared to a predictive model to identify the type of theoretical nonlinearity (i.e. quadratic or cubic stiffness, quadratic or cubic damping, etc.) that was present. The predictive model is based on a nonlinear, single degree-of-freedom system. Nonlinear features in the response of the system were identified for different types of stiffness and damping nonlinearities. The experimentally measured response was analyzed to see which of these features were present. Based on this analysis, the response of the panel of damage due to core-crushing indicated a quadratic spring-type stiffness.Copyright