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1.

The machining process is primarily used to remove material using cutting tools. Any variation in tool state affects the quality of a finished job and causes disturbances. So, a tool monitoring scheme (TMS) for categorization and supervision of failures has become the utmost priority. To respond, traditional TMS followed by the machine learning (ML) analysis is advocated in this paper. Classification in ML is supervised based learning method wherein the ML algorithm learn from the training data input fed to it and then employ this model to categorize the new datasets for precise prediction of a class and observation. In the current study, investigation on the single point cutting tool is carried out while turning a stainless steel (SS) workpeice on the manual lathe trainer. The vibrations developed during this activity are examined for failure-free and various failure states of a tool. The statistical modeling is then incorporated to trace vital signs from vibration signals. The multiple-binary-rule-based model for categorization is designed using the decision tree. Lastly, various tree-based algorithms are used for the categorization of tool conditions. The Random Forest offered the highest classification accuracy, i.e., 92.6%.

  相似文献   
2.
由于海啸波具有长周期、慢衰减的特征, 对于能够激发海啸的海底地震, 海啸波资料一定程度上可以作为利用地震波资料和大地测量资料反演得到的震源有限断层模型的补充数据, 以验证该模型的合理性. 本文以2011年日本东北大地震海啸期间反演的震源模型为例, 利用Okada弹性半无限空间理论将震源有限断层模型转化为垂向海底变形; 然后利用GeoClaw海啸数值模型进行海啸数值模拟, 在计算区域模拟实际观测点的海啸波波形数据; 并将模拟海啸波波形数据与实际观测值进行对比, 以验证震源有限断层模型的可靠性.  相似文献   
3.
A new approach for tree-level amplitudes with multiple fermion lines is presented. It primarily focuses on the simplification of fermion lines. By calculating two vectors recursively without any matrix multiplications, the result of a fermion line is reduced to a very compact form depending only on the two vectors. Comparisons with other packages are presented, and the results show that our package FDC provides a very good performance in the processes of multiple fermion lines with this new approach and some other improvements. A further comparison with WHIZARD shows that this new approach has a competitive efficiency in computing pure amplitude squares without phase space integration.  相似文献   
4.
Lin Lang 《中国物理 B》2022,31(12):126102-126102
High-entropy alloys (HEAs) and medium-entropy alloys (MEAs) have attracted a great deal of attention for developing nuclear materials because of their excellent irradiation tolerance. Herein, formation and evolution of radiation-induced defects in NiCoFe MEA and pure Ni are investigated and compared using molecular dynamics simulation. It is observed that the defect recombination rate of ternary NiCoFe MEA is higher than that of pure Ni, which is mainly because, in the process of cascade collision, the energy dissipated through atom displacement decreases with increasing the chemical disorder. Consequently, the heat peak phase lasts longer, and the recombination time of the radiation defects (interstitial atoms and vacancies) is likewise longer, with fewer deleterious defects. Moreover, by studying the formation and evolution of dislocation loops in Ni-Co-Fe alloys and Ni, it is found that the stacking fault energy in Ni-Co-Fe decreases as the elemental composition increases, facilitating the formation of ideal stacking fault tetrahedron structures. Hence, these findings shed new light on studying the formation and evolution of radiation-induced defects in MEAs.  相似文献   
5.
With the quick development of sensor technology in recent years, online detection of early fault without system halt has received much attention in the field of bearing prognostics and health management. While lacking representative samples of the online data, one can try to adapt the previously-learned detection rule to the online detection task instead of training a new rule merely using online data. As one may come across a change of the data distribution between offline and online working conditions, it is challenging to utilize the data from different working conditions to improve detection accuracy and robustness. To solve this problem, a new online detection method of bearing early fault is proposed in this paper based on deep transfer learning. The proposed method contains an offline stage and an online stage. In the offline stage, a new state assessment method is proposed to determine the period of the normal state and the degradation state for whole-life degradation sequences. Moreover, a new deep dual temporal domain adaptation (DTDA) model is proposed. By adopting a dual adaptation strategy on the time convolutional network and domain adversarial neural network, the DTDA model can effectively extract domain-invariant temporal feature representation. In the online stage, each sequentially-arrived data batch is directly fed into the trained DTDA model to recognize whether an early fault occurs. Furthermore, a health indicator of target bearing is also built based on the DTDA features to intuitively evaluate the detection results. Experiments are conducted on the IEEE Prognostics and Health Management (PHM) Challenge 2012 bearing dataset. The results show that, compared with nine state-of-the-art fault detection and diagnosis methods, the proposed method can get an earlier detection location and lower false alarm rate.  相似文献   
6.
When applying a diagnostic technique to complex systems, whose dynamics, constraints, and environment evolve over time, being able to re-evaluate the residuals that are capable of detecting defaults and proposing the most appropriate ones can quickly prove to make sense. For this purpose, the concept of adaptive diagnosis is introduced. In this work, the contributions of information theory are investigated in order to propose a Fault-Tolerant multi-sensor data fusion framework. This work is part of studies proposing an architecture combining a stochastic filter for state estimation with a diagnostic layer with the aim of proposing a safe and accurate state estimation from potentially inconsistent or erroneous sensors measurements. From the design of the residuals, using α-Rényi Divergence (α-RD), to the optimization of the decision threshold, through the establishment of a function that is dedicated to the choice of α at each moment, we detail each step of the proposed automated decision-support framework. We also dwell on: (1) the consequences of the degree of freedom provided by this α parameter and on (2) the application-dictated policy to design the α tuning function playing on the overall performance of the system (detection rate, false alarms, and missed detection rates). Finally, we present a real application case on which this framework has been tested. The problem of multi-sensor localization, integrating sensors whose operating range is variable according to the environment crossed, is a case study to illustrate the contributions of such an approach and show the performance.  相似文献   
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8.
为调查浙江省部分地区虾肝肠胞虫(Enterocytozoon hepatopenaei, EHP)暴发来源及与其他宿主来源的微孢子虫进化关系, 本研究利用形态学特征和分子鉴定, 尝试对引起浙江多地凡纳对虾生长缓慢综合征的病原进行探究. 同时基于18S rRNA基因序列分析该病原与其他地区及其他宿主来源微孢子虫的进化关系. 结果显示, 所采病虾均为EHP阳性, 采样地区EHP 18S rRNA基因具有高度同源性, 为98.5%~100%. 对国内外EHP基因序列进行同源性分析, 其同源性为86.2%~100%, 所采EHP与印度EHP聚为一个分支. EHP 18S rRNA基因序列与其他水生生物微孢子虫18S rRNA均处于不同分支, 表明18S rRNA可作为EHP的主要鉴别序列. 将本研究所采EHP与NCBI中同源性较高的比氏肠胞虫和金头鲷微孢子虫比较分析, 结果表明微孢子虫基因序列同源性与宿主进化发育程度有关, 宿主之间进化距离越近, 则微孢子虫基因序列同源性相聚越近.  相似文献   
9.
具有n个顶点且度序列为(m,2,…,2,1,…,1)(1的重数为m)的连通图不止一个(这些图均为树),而每个树对应唯一一个段序列(l1,l2,…,lm).通过对任意一树移动最长段的悬挂点到最短段悬挂点的方式得到另一树,比较前后两树的覆盖成本和反向覆盖成本,给出了具有最小覆盖成本和反向覆盖成本的极树,并且进一步给出了取得最小覆盖成本和反向覆盖成本的顶点.  相似文献   
10.
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