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现场可编程门阵列(FPGA)内部资源众多,其中互连资源出现故障的概率远远高于片内其他资源,而在以往许多互连测试研究中,所生成的测试配置存在无法覆盖反馈桥接故障的难题,所以较难有测试配置实现故障列表的100%覆盖。因此通过约束桥接故障只发生在单个查找表(LUT)内的信号线上,并结合单项函数,对反馈桥接故障模型进行优化改进,从根本上解决难题;然后对优化后的反馈桥接故障设置相应的约束条件,再使用布尔可满足性理论(SAT)生成满足约束条件的测试配置。采用优化后的故障模型对ISCAS"89基准电路进行了测试配置生成实验,结果表明生成的测试向量解决了反馈桥接故障的覆盖难题,并且在实现故障列表的100%覆盖下,优化后的故障模型所需要的测试配置数最少。 相似文献
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Machine learning algorithms have been widely used in mine fault diagnosis. The correct selection of the suitable algorithms is the key factor that affects the fault diagnosis. However, the impact of machine learning algorithms on the prediction performance of mine fault diagnosis models has not been fully evaluated. In this study, the windage alteration faults (WAFs) diagnosis models, which are based on K-nearest neighbor algorithm (KNN), multi-layer perceptron (MLP), support vector machine (SVM), and decision tree (DT), are constructed. Furthermore, the applicability of these four algorithms in the WAFs diagnosis is explored by a T-type ventilation network simulation experiment and the field empirical application research of Jinchuan No. 2 mine. The accuracy of the fault location diagnosis for the four models in both networks was 100%. In the simulation experiment, the mean absolute percentage error (MAPE) between the predicted values and the real values of the fault volume of the four models was 0.59%, 97.26%, 123.61%, and 8.78%, respectively. The MAPE for the field empirical application was 3.94%, 52.40%, 25.25%, and 7.15%, respectively. The results of the comprehensive evaluation of the fault location and fault volume diagnosis tests showed that the KNN model is the most suitable algorithm for the WAFs diagnosis, whereas the prediction performance of the DT model was the second-best. This study realizes the intelligent diagnosis of WAFs, and provides technical support for the realization of intelligent ventilation. 相似文献
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One of the major challenges in wireless body area networks (WBANs) is sensor fault detection. This paper reports a method for the precise identification of faulty sensors, which should help users identify true medical conditions and reduce the rate of false alarms, thereby improving the quality of services offered by WBANs. The proposed sensor fault detection (SFD) algorithm is based on Pearson correlation coefficients and simple statistical methods. The proposed method identifies strongly correlated parameters using Pearson correlation coefficients, and the proposed SFD algorithm detects faulty sensors. We validated the proposed SFD algorithm using two datasets from the Multiparameter Intelligent Monitoring in Intensive Care database and compared the results to those of existing methods. The time complexity of the proposed algorithm was also compared to that of existing methods. The proposed algorithm achieved high detection rates and low false alarm rates with accuracies of 97.23% and 93.99% for Dataset 1 and Dataset 2, respectively. 相似文献
6.
Leo H. Chiang Birgit Braun Zhenyu Wang Ivan Castillo 《American Institute of Chemical Engineers》2022,68(6):e17644
In the Industry 4.0 era, the chemical industry is embracing broad adoption of artificial intelligence (AI) and machine learning (ML) methods. This article provides a holistic view of how the industry is transforming digitally towards AI at scale. First, a historical perspective on how the industry used AI to aid humans in better decision-making is shown. Then state-of-the-art AI research addressing industrial needs on reliability and safety, process optimization, supply chain, material discovery, and reaction engineering is highlighted. Finally, a vision of the plant of the future is illustrated with critical components of AI-ready culture, model life cycle management, and renewed role of humans in chemical manufacturing. 相似文献
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应用动态模拟软件,建立LNG槽车满载和空载运输系统动态模型,研究LNG槽车运输过程中储罐内操作压力、操作温度和液位的动态变化趋势。分析表明:LNG槽车满载长距离运输储罐压力上升较小,不易造成储罐超压泄放,安全性较高;空载运输较满载运输储罐升压速率更快;LNG储罐空载液位5%可实现LNG槽车储罐处于冷却状态。 相似文献
9.
The present study proposes an algorithm for fault detection in terms of condition‐based maintenance with data mining techniques. The proposed algorithm is applied on an aircraft turbofan engine using flight data and consists of two main sections. In the first section, the relationship between engine exhaust gas temperature (EGT) as the main engine health monitoring criterion and other operational and environmental parameters of the engine was modelled using the data‐driven models. In the second section, a data set including EGT residuals, that is, the difference between the actual EGT of the system and the EGT estimated by the developed model in the health conditions of the engine, was created. Finally, faults occurring in each flight were detected based on the identification of abnormal events by a one‐class support vector machine trained by the health condition EGT residual data set. The results indicated that the proposed algorithm was an effective approach for inspecting aircraft engine conditions and detecting faults, with no need for technical knowledge on the interior characteristics of the aircraft engine. 相似文献
10.
塔里木盆地顺1走滑断裂带超深储层油气充注历史 总被引:3,自引:0,他引:3
顺北地区是近期塔里木盆地超深层油气勘探获得重大突破的领域。通过顺北地区奥陶系一间房组储层原油和天然气地化特征对比,并结合荧光颜色和显微荧光光谱参数分析,认为顺北地区奥陶系经历了多期油气充注,且早期充注原油遭受一定程度的生物降解,而天然气为原油伴生气。利用类异戊二烯烃、生物标志化合物和原油轻烃对顺北地区和顺南地区原油进行对比,发现顺北地区油气来源与顺南地区一样来自于寒武系玉尔吐斯组烃源岩,通过断穿寒武系的断层向上运移至奥陶系储集层。对顺北地区采集样品进行流体包裹体测试、冷阴极发光、荧光观察等系统分析,结果表明顺北地区发生了“五幕三期”油充注和一期天然气充注,第一期为加里东晚期,第二期为海西晚期,第三期为喜马拉雅晚期,而天然气充注发生在喜马拉雅晚期。 相似文献