314 research outputs found

    Missile Modeling and Simulation of Nominal and Abnormal Scenarios Resulting from External Damage

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    This thesis presents the development of a six-degree-of-freedom flight simulation environment for missiles and the application thereof to investigate the flight performance of missiles when exposed to external damage. The simulation environment was designed to provide a realistic representation of missile flight dynamics including aerodynamic effects, flight control systems, and self-guidance. The simulation environment was designed to be modular, expandable, and include realistic models of external damage to the missile body obtained by adversarial counteraction. The primary objective of this research was to examine missile flight performance when subjected to unspecified external damage, including changes in trajectory, stability, and controllability, and to provide a basis for the future development of fault tolerant control laws to improve target tracking and overall flight performance when experiencing abnormal conditions. To accomplish this, a variety of scenarios were developed to simulate damage to different parts of the missile, such as the fuselage, wings, and control surfaces. Three types of damage are considered: arbitrary failures which affect the major overall missile dynamic force and moment coefficients, structural failures including wings and fin breakage, and stuck fin failures where a given fin is arbitrarily fixed to a specified deflection. The missile behavior in response to these scenarios was analyzed and compared to the baseline behavior of an undamaged missile. The results of this research demonstrate how simulated missiles behave during flight, under both nominal and abnormal scenarios resulting from external damage. The simulation environment is shown to be a useful tool in examining the performance of missiles under real-world scenarios, such as during combat, in the event of an accident, or when exposed to other adversarial counteractions. This is done by producing envelopes for mission success for each tested scenario and analyzing the results. The results of this research can be used to assist in and improve the design and performance of missiles and enhance their survivability in the field. These results can also be used to determine the amount of damage necessary to prevent a given missile from reaching its target

    Stray Flux Monitoring and Multi-Sensor Fusion Condition Monitoring for Squirrel Cage Induction Machines

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    This research work investigates the ability of external magnetic flux-based condition monitoring to detect rotor-related faults and incipient stage bearing faults in squirrel-cage induction machines (SCIMs). This work also discusses the multisensory synergy of the external magnetic flux measurement with other measurements. To investigate the stray flux-based monitoring technique, this dissertation presents a theoretical analysis of the characteristic components in the stray flux spectrum of SCIMs as well as experimental validations. A wavelet packet decomposition (WPD) denoising method is proposed for flux-based incipient bearing fault detection. Additionally, a sensor fusion method to efficiently utilize the information from heterogeneous sensor measurements (external magnetic flux and stator current) to achieve higher rotor-related fault detection sensitivity and a higher fault type recognition rate is presented. Instead of using all the characteristic components directly, the proposed fusion method groups the features of several rotor abnormalities and then draws a conclusion on machine health status based on the abnormalities that are present in the machine. Finally, a novel sensor fusion-based rotor vibration observer method is proposed for incipient bearing fault detection. The observer can reject the electrical disturbances from the supply side. Meanwhile, the proposed observer is less affected by the mechanical noise from lousy environment than using vibration-based monitoring.Ph.D

    Artificial Intelligence Supported EV Electric Powertrain for Safety Improvement

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    As an environmentally friendly transport option, electric vehicles (EVs) are endowed with the characteristics of low fossil energy consumption and low pollutant emissions. In today's growing market share of EVs, the safety and reliability of the powertrain system will be directly related to the safety of human life. Reliability problems of EV powertrains may occur in any power electronic (PE) component and mechanical part, both sudden and cumulative. These faults in different locations and degrees will continuously threaten the life of drivers and pedestrians, bringing irreparable consequences. Therefore, monitoring and predicting the real-time health status of EV powertrain is a high-priority, arduous and challenging task. The purposes of this study are to develop AI-supported effective safety improvement techniques for EV powertrains. In the first place, a literature review is carried out to illustrate the up-to-date AI applications for solving condition monitoring and fault detection issues of EV powertrains, where recent case studies between conventional methods and AI-based methods in EV applications are compared and analysed. On this ground this study, then, focuses on the theories and techniques concerning this topic so as to tackle different challenges encountered in the actual applications. In detail, first, as for diagnosing the bearing system in the earlier fault period, a novel inferable deep distilled attention network is designed to detect multiple bearing faults. Second, a deep learning and simulation driven approach that combines the domain-adversarial neural network and the lumped-parameter thermal network (LPTN) is proposed for achieve IPMSM permanent magnet temperature estimation work. Finally, to ensure the use safety of the IGBT module, deep learning -based IGBT modules’ double pulse test (DPT) efficiency enhancement is proposed and achieved via multimodal fusion networks and graph convolution networks

    PROCEEDINGS 5th PLATE Conference

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    The 5th international PLATE conference (Product Lifetimes and the Environment) addressed product lifetimes in the context of sustainability. The PLATE conference, which has been running since 2015, has successfully been able to establish a solid network of researchers around its core theme. The topic has come to the forefront of current (political, scientific & societal) debates due to its interconnectedness with a number of recent prominent movements, such as the circular economy, eco-design and collaborative consumption. For the 2023 edition of the conference, we encouraged researchers to propose how to extend, widen or critically re-construct thematic sessions for the PLATE conference, and the paper call was constructed based on these proposals. In this 5th PLATE conference, we had 171 paper presentations and 238 participants from 14 different countries. Beside of paper sessions we organized workshops and REPAIR exhibitions

    Advanced signal processing methods for condition monitoring

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    Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park's Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.Web of Scienc

    Measuring knowledge sharing processes through social network analysis within construction organisations

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    The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes

    Mining Technologies Innovative Development

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    The present book covers the main challenges, important for future prospects of subsoils extraction as a public effective and profitable business, as well as technologically advanced industry. In the near future, the mining industry must overcome the problems of structural changes in raw materials demand and raise the productivity up to the level of high-tech industries to maintain the profits. This means the formation of a comprehensive and integral response to such challenges as the need for innovative modernization of mining equipment and an increase in its reliability, the widespread introduction of Industry 4.0 technologies in the activities of mining enterprises, the transition to "green mining" and the improvement of labor safety and avoidance of man-made accidents. The answer to these challenges is impossible without involving a wide range of scientific community in the publication of research results and exchange of views and ideas. To solve the problem, this book combines the works of researchers from the world's leading centers of mining science on the development of mining machines and mechanical systems, surface and underground geotechnology, mineral processing, digital systems in mining, mine ventilation and labor protection, and geo-ecology. A special place among them is given to post-mining technologies research
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