Vibration measurement based condition monitoring

Vibrations of the transformers are complex multi-physics phenomena that require a deep understanding of electromagnetic and mechanical principles. Their analysis can be used to assess the condition of the transformer in terms of mechanical fixation quality, buckling or ageing of the components. The article presents the 20 years of efforts of researchers in Xi\u27an Jiaotong University and The University of Queensland on transformer vibration characteristics and its application in the winding mechanical condition monitoring

Vibration measurement based condition monitoring

Vibrations of the transformers are complex multi-physics phenomena that require a deep understanding of electromagnetic and mechanical principles. Their analysis can be used to assess the condition of the transformer in terms of mechanical fixation quality, buckling or ageing of the components. The article presents the 20 years of efforts of researchers in Xi\u27an Jiaotong University and The University of Queensland on transformer vibration characteristics and its application in the winding mechanical condition monitoring

See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data

Zero-shot point cloud segmentation aims to make deep models capable of recognizing novel objects in point cloud that are unseen in the training phase. Recent trends favor the pipeline which transfers knowledge from seen classes with labels to unseen classes without labels. They typically align visual features with semantic features obtained from word embedding by the supervision of seen classes' annotations. However, point cloud contains limited information to fully match with semantic features. In fact, the rich appearance information of images is a natural complement to the textureless point cloud, which is not well explored in previous literature. Motivated by this, we propose a novel multi-modal zero-shot learning method to better utilize the complementary information of point clouds and images for more accurate visual-semantic alignment. Extensive experiments are performed in two popular benchmarks, i.e., SemanticKITTI and nuScenes, and our method outperforms current SOTA methods with 52% and 49% improvement on average for unseen class mIoU, respectively.Comment: Accepted by ICCV 202

Intrinsically unidirectional chemically fuelled rotary molecular motors

Biological systems mainly utilize chemical energy to fuel autonomous molecular motors, enabling the system to be driven out of equilibrium1. Taking inspiration from rotary motors such as the bacterial flagellar motor2 and adenosine triphosphate synthase3, and building on the success of light-powered unidirectional rotary molecular motors4–6, scientists have pursued the design of synthetic molecular motors solely driven by chemical energy7–13. However, designing artificial rotary molecular motors operating autonomously using a chemical fuel and simultaneously featuring the intrinsic structural design elements to allow full 360° unidirectional rotary motion like adenosine triphosphate synthase remains challenging. Here we show that a homochiral biaryl Motor-3, with three distinct stereochemical elements, is a rotary motor that undergoes repetitive and unidirectional 360° rotation of the two aryl groups around a single-bond axle driven by a chemical fuel. It undergoes sequential ester cyclization, helix inversion and ring opening, and up to 99% unidirectionality is realized over the autonomous rotary cycle. The molecular rotary motor can be operated in two modes: synchronized motion with pulses of a chemical fuel and acid–base oscillations; and autonomous motion in the presence of a chemical fuel under slightly basic aqueous conditions. This rotary motor design with intrinsic control over the direction of rotation, simple chemical fuelling for autonomous motion and near-perfect unidirectionality illustrates the potential for future generations of multicomponent machines to perform mechanical functions

LogPrompt: Prompt Engineering Towards Zero-Shot and Interpretable Log Analysis

Automated log analysis is crucial in modern software-intensive systems for ensuring reliability and resilience throughout software maintenance and engineering life cycles. Existing methods perform tasks such as log parsing and log anomaly detection by providing a single prediction value without interpretation. However, given the increasing volume of system events, the limited interpretability of analysis results hinders analysts' trust and their ability to take appropriate actions. Moreover, these methods require substantial in-domain training data, and their performance declines sharply (by up to 62.5%) in online scenarios involving unseen logs from new domains, a common occurrence due to rapid software updates. In this paper, we propose LogPrompt, a novel zero-shot and interpretable log analysis approach. LogPrompt employs large language models (LLMs) to perform zero-shot log analysis tasks via a suite of advanced prompt strategies tailored for log tasks, which enhances LLMs' performance by up to 107.5% compared with simple prompts. Experiments on nine publicly available evaluation datasets across two tasks demonstrate that LogPrompt, despite using no training data, outperforms existing approaches trained on thousands of logs by up to around 50%. We also conduct a human evaluation of LogPrompt's interpretability, with six practitioners possessing over 10 years of experience, who highly rated the generated content in terms of usefulness and readability (averagely 4.42/5). LogPrompt also exhibits remarkable compatibility with open-source and smaller-scale LLMs, making it flexible for practical deployment

ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection

Anomaly detection in multivariate time series data is of paramount importance for ensuring the efficient operation of large-scale systems across diverse domains. However, accurately detecting anomalies in such data poses significant challenges. Existing approaches, including forecasting and reconstruction-based methods, struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capturing complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process. We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observe a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.Comment: To appear in VLDB 2024.Code: https://github.com/17000cyh/IMDiffusion.gi

Progressive collapse resistance mechanism of RC frame structure considering reinforcement corrosion

Corrosion causes reduction in cross-sectional area of reinforcement, deterioration of mechanical properties, and degradation of bonding properties between reinforced concrete, which are the most important factors leading to the degradation of structural service performance. In order to investigate the progressive collapse mechanism of a corroded reinforced concrete frame structure, the failure modes, characteristics of the vertical displacement, and load capacity are studied using the finite element method. Based on existing experimental research, the established model is verified, and the influence of different influencing factors on the progressive collapse mechanism is analyzed. The results show that the corrosion of the reinforcement affects the yield load, peak load, and ultimate load of the reinforced concrete substructure. As the corrosion rate increases, the tensile arch action shows a particularly severe deterioration. The variation of concrete strength and the height–span ratio affects the substructure’s load-bearing capacity much more significantly than the stirrup spacing

Publisher Correction: Copper adparticle enabled selective electrosynthesis of n-propanol.

An amendment to this paper has been published and can be accessed via a link at the top of the paper