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Abstract Electronic components that undergo shock and vibration are susceptible to failure caused by damage in the base printed circuit board that makes up the substrate of these systems. In certain applications, it may become paramount to know in real-time if the electronic components are damaged to enable a next-generation active system to take immediate responses. Broad examples of such systems include blast mitigation systems or safety systems in car accidents. These systems on classified under the term “high-rate” as they experience high shock levels on short time scales. This work proposes a long short-term memory neural network to enable real-time damage detection and assessment of electronic assemblies subjected to shock. The long short-term memory neural network is able to infer the state of the structure in approximately 4 milliseconds following the impact. The model obtains perfect classification results at 4 milliseconds for the data used in this work. This work is supported by experimentation that indicates damage to electronic packages can be quantified through the in situ monitoring of the impedance of electrical connections. Changes in impedance correlate to alterations in the physical properties of electronic components which indicate the occurrence of damage. On this basis, a comprehensive dataset is created to monitor the impedance changes of a daisy-chained connection through repeated high-energy shocks. Meanwhile, the shock response of the electronic components is captured using an accelerometer, enabling a detailed analysis of the effects of high-rate shock on the components’ performance. A dataset is developed to encompass 30 repeated impacts experiencing 10,000 gn during impact with an average half-sine time of 322 microseconds. The paper outlines the proposed real-time machine learning framework while performance metrics are presented and discussed in detail. 

Abstract To enable real-time control of next-generation active structures during shock events, there is a need to identify the start of a shock event within microseconds of its initiation. The delayed classification of a shock event may cause damage to the system that could have been prevented with assumed next-generation active control mechanisms. Addressing the challenge of ultra-low latency shock event classification requires utilizing prior information on normal behaviors (i.e., the system under vibrational loading) to identify abnormalities that can be classified as features of a shock event. The purpose of changepoint shock classification is to automatically recognize when a structure of interest behaves differently than expected in some measurable way. In this work, we analyze two different methods for shock classification using changepoint methodologies. We study the use of adaptive cumulative summation and expectation maximization algorithms in this work. Each method presents advantages and disadvantages for different scenarios. This study aims to derive features (streams of time series data) for the changepoint algorithms and revise the changepoint models to be used in real-time robust shock event detection. In this work, a printed circuit board under continuous vibrations before, during, and after a shock event is used to investigate the proposed methodologies. The printed circuit board is monitored with an accelerometer that is used to monitor both the vibrational and shock state of the system. The vibrational response of the system consists of accelerations up to 20 m/s2, while the shock event consists of loadings up to 2,000 m/s2. This work showed that the CUSUM algorithm is fairly effective at identifying the shock state in data but generates many false positives during normal behavior times, with no false positives post-shock, indicating accurate shock state detection despite early errors. In contrast, the Expectation Maximization (EM) algorithm shows improved performance by correctly predicting no shock in the initial phase and accurately identifying the onset of the shock state. It occasionally misclassifies shocked points as normal due to its change point identification process. Compared to CUSUM, EM has fewer false positives before the shock and similar performance during and after the shock event. Future research efforts will focus on developing online versions of these algorithms, which can identify system states with a minimum number of errors. The limitations of the system and its robustness to noise are discussed. 

Modeling dry friction is a challenging task. Accurate models must incorporate hysteretic rise of force across displacement and non-linearity from the Stribeck effect. Though sufficiently accurate models have been proposed for simple friction systems where these two effects dominate, certain rotational friction systems introduce self-energizing and accompanying backlash effects. These systems are termed self-energizing systems. In these systems, the friction force is amplified by a mechanical advantage which is charged through motion and released during reversing the direction of travel. This produces energized and backlash regimes within which the friction device follows different dynamic behaviors. This paper examines self-energizing rotational friction, and proposes a combined physics and machine learning approach to produce a unified model for energized and backlash regimes. In this multi-process information fusion methodology, a classical LuGre friction model is augmented to allow state-dependent parameterization provided by a machine learning model. The method for training the model from experimental data is given, and demonstrated with a 20 kN banded rotary friction device used for structural control. Source code replicating the methodology is provided. Results demonstrate that the combined model is capable of reproducing the backlash effect and reduces error compared to the standard LuGre model by a cumulative 32.8%; in terms modeling the tested banded rotary friction device. In these experimental tests, realistic pre-defined displacements inputs are used to validate the damper. The output of the machine learning model is analyzed and found to align with the physical understanding of the banded rotary friction device. 

Electric Aircraft have the potential to revolutionize short-distance air travel with lower operating costs and simplified maintenance. However, due to the long lead-time associated with procuring batteries and the maintenance challenges of replacing and repairing batteries in electric aircraft, there are still unanswered questions related to the true long-term operating costs of electric aircraft. This research examines using a load-sharing system in electric aircraft to optimally tune battery degradation in a multi-battery system such that the battery life of a single battery is extended. The active optimization of energy drawn from multiple battery packs means that each battery pack reaches its optimal replacement point at the same time; thereby simplifying the maintenance procedure and reducing cost. This work uses lithium iron phosphate batteries experimentally characterized and simulated in OpenModelica for a flight load profile. Adaptive agents control the load on the battery according to factors such as state of charge, and state of health, to respond to potential faults. The findings in this work show the potential for adaptive agents to selectively draw more power from a healthy battery to extend the lifespan of a degraded battery such that the remaining useful life of both batteries reaches zero at the same time. Simulations show that dual battery replacement can be facilitated using the proposed method when the in-service battery has a remaining useful life of greater than 0.5; assuming that the replacement battery it is paired with has a remaining useful life of 1.0. Limitations of the proposed method are discussed within this work. 

This paper introduces a novel approach to enhance the docking mechanism of sensor packages deployed on bridges using unmanned aerial vehicles (UAVs). The current electropermanent magnet (EPM) system faces challenges in achieving efficient and stable docking due to factors such as airflow, GPS stabilization, and the time required for EPM activation. To address these issues, a biased EPM design is proposed, utilizing additional permanent magnets to achieve neutral buoyancy during UAV deployment. This design optimally balances the weight of the drone and sensor package, providing advantages such as improved stability against external factors and reduced pilot fatigue. Experimental results demonstrate the feasibility of the proposed design, indicating enhanced hold force and an extended range for efficient docking. 

In high-rate structural health monitoring, it is crucial to quickly and accurately assess the current state of a component under dynamic loads. State information is needed to make informed decisions about timely interventions to prevent damage and extend the structure’s life. In previous studies, a dynamic reproduction of projectiles in ballistic environments (DROPBEAR) testbed was used to evaluate the accuracy of state estimation techniques through dynamic analysis. This paper extends previous research by incorporating the local eigenvalue modification procedure (LEMP) and data fusion techniques to create a more robust state estimate using optimal sampling methodologies. The process of estimating the state involves taking a measured frequency response of the structure, proposing frequency response profiles, and accepting the most similar profile as the new mean for the position estimate distribution. Utilizing LEMP allows for a faster approximation of the proposed model with linear time complexity, making it suitable for 2D or sequential damage cases. The current study focuses on two proposed sampling methodology refinements: distilling the selection of candidate test models from the position distribution and applying a Kalman filter after the distribution update to find the mean. Both refinements were effective in improving the position estimate and the structural state accuracy, as shown by the time response assurance criterion and the signal-to-noise ratio with up to 17% improvement. These two metrics demonstrate the benefits of incorporating data fusion techniques into the high-rate state identification process.