DETECTION OF FATIGUE DAMAGE IN SHEAR CONNECTIONS USING ACOUSTIC WAVE PROPAGATION

Fatigue damage is an important concern in structural steel connections where frequent load reversals are expected. Detection of fatigue damage is typically done with infrequent visual inspections that are subjective and limited to surface features. A permanent embedded structural health monitoring (SHM) system could be helpful in detecting damage as it occurs. Current methods for detecting fatigue damage include inference of fatigue life expended through cycle counting from long-term strain measurement campaigns, and through short-term impedance measurements. The first approach has the advantage that it is relatively simple from an algorithmic point of view, but it is an indirect measure of damage, and it does require that strain gauges be present and operational to measure the entire strain history of the component in question. Impedance measurements from piezoelectric transducers (PZTs) bonded to the surface of the specimen can detect damage directly and do not require the use of historical data (though baseline health impedance signatures are needed for reference), but the processing of impedance data in this application can be difficult and subjective. In addition, many studies focused on impedance measurements for damage detection using overly simplified coupon geometries for experimental validation that do not capture the full complexity of a structural steel connection. In this study, an acoustic wave propagation method is proposed to detect fatigue damage in a bolted seated connection. A PZT located on the connected column measures the energy that propagates through the connection. Only the top angle for the connection (typically used for stability) is damaged in the study to allow multiple tests to be made with minimal specimen preparation. Signal processing methods including vii matched filter to separate the input signal from the signal distortions are used to improve the sensitivity of the approach. Features examined include energy transmitted through the connection and pole information associated with the signal residuals (error information) with only the latter being sensitive to fatigue damage

A multi-fault diagnosis method for piston pump in construction machinery based on information fusion and PSO-SVM

Piston pumps are key components in construction machinery, the failure of which may cause long delay of the construction work and even lead to serious accident. Because construction machines are exposed to poor working conditions, multiple faults of piston pumps are most likely to occur simultaneously. When multiple faults occur together, it is difficult to detect. A multi-fault diagnosis method for piston pump based on information fusion and PSO-SVM is proposed in this thesis. Information fusion is used as fault feature extraction and PSO-SVM is applied as the fault mode classifier. According to the method, vibration signal and pressure signal of piston pump in normal state, single fault state and multi-fault state are collected at first. Then the empirical mode decomposition (EMD) is used to decompose vibration signals into different frequency band and energy features are extracted. These energy features extracted from vibration signals and time-domain features extracted from pressure signal are information fused at the feature layer and constitute the eigenvectors. Finally, these eigenvectors are put into support vector machine (SVM) and the working conditions of piston pump were classified. Particle swarm optimization (PSO) is applied to optimize two parameters of SVM. The experimental results show that the recognition accuracy of the normal state, three single failure modes and multi-fault modes are 98.3 %, 97.6 % and 94 % respectively. These recognition accuracies are higher than which using vibration signal or pressure signal alone. So, the proposed method can not only identify the single fault, but also effectively identify the multi-fault of piston pump

On the Adversarial Robustness of Camera-based 3D Object Detection

In recent years, camera-based 3D object detection has gained widespread attention for its ability to achieve high performance with low computational cost. However, the robustness of these methods to adversarial attacks has not been thoroughly examined. In this study, we conduct the first comprehensive investigation of the robustness of leading camera-based 3D object detection methods under various adversarial conditions. Our experiments reveal five interesting findings: (a) the use of accurate depth estimation effectively improves robustness; (b) depth-estimation-free approaches do not show superior robustness; (c) bird's-eye-view-based representations exhibit greater robustness against localization attacks; (d) incorporating multi-frame benign inputs can effectively mitigate adversarial attacks; and (e) addressing long-tail problems can enhance robustness. We hope our work can provide guidance for the design of future camera-based object detection modules with improved adversarial robustness

Interpretable Math Word Problem Solution Generation Via Step-by-step Planning

Solutions to math word problems (MWPs) with step-by-step explanations are valuable, especially in education, to help students better comprehend problem-solving strategies. Most existing approaches only focus on obtaining the final correct answer. A few recent approaches leverage intermediate solution steps to improve final answer correctness but often cannot generate coherent steps with a clear solution strategy. Contrary to existing work, we focus on improving the correctness and coherence of the intermediate solutions steps. We propose a step-by-step planning approach for intermediate solution generation, which strategically plans the generation of the next solution step based on the MWP and the previous solution steps. Our approach first plans the next step by predicting the necessary math operation needed to proceed, given history steps, then generates the next step, token-by-token, by prompting a language model with the predicted math operation. Experiments on the GSM8K dataset demonstrate that our approach improves the accuracy and interpretability of the solution on both automatic metrics and human evaluation.Comment: Accepted to The 61st Annual Meeting of the Association for Computational Linguistics (ACL 2023