CDBN‑IKELM的轴承变工况故障诊断方法

针对现有方法在轴承变工况方面存在的诊断精度低、人工提取特征不充分等问题，提出了基于卷积深度置信网络（convolutional deep belief network，简称CDBN）与改进核极限学习机 （improved Kernel?based extreme learning machine，简称IKELM）的滚动轴承故障智能识别方法。首先，由卷积深度置信网络对原始信号内的故障特征进行深层自适应提取；其次，利用等距特征映射对提取的多维特征进行降维，去除冗余特征信息；然后，采用改进的核极限学习机对特征进行分类，使用粒子群（particle swarm optimization， 简称PSO）对模型重要参数进行优化，实现滚动轴承变工况下的故障识别；最后，将所提方法应用于不同工况下多种轴承故障的诊断。实验结果表明，该方法能够智能有效地识别变工况的轴承故障，诊断结果优于已有的智能故障诊断方法。     

Implication of Size-Controlled Graphite Nanosheets as Building Blocks for Thermal Conductive Three-Dimensional Framework Architecture of Nanocarbons

Preparation of three-dimensional (3D) networks has received significant attention as an effective approach for applications involving transport phenomena, such as thermal management materials, and several nanomaterials have been examined as potential building blocks of 3D networks for the improvement of heat conduction in polymer nanocomposites. For that purpose, nanocarbons such as graphene and graphite nanoplatelets have been spotlighted as suitable filler materials because of their excellent thermal conductivities (ca. 10²–10³ W·(m·K)^?1 along their lateral axes) and morphological merits. However, the implications of morphological features such as the lateral length and thickness of graphene or graphene-like materials have not yet been identified. In this study, a controlled dissociation of bulk graphite to graphite nanosheets (GNSs) using a low-cost, ecofriendly bead mill process was extensively examined and, when configured in a 3D framework architecture formation, the size-controlled GNSs demonstrated that the thermal conductivities of a 3D interconnected framework of GNSs and the corresponding polymer nanocomposite were intimately correlated with the size of the GNSs, thus demonstrating the successful preparation of an efficient thermal management material without highly sophisticated efforts. The capability of controlling the lateral size and thickness of the GNSs as well as the use of a 3D interconnected framework architecture should greatly assist the commercialization of high-quality graphene-based thermal management materials in a scalable production process.     

贯筋藤凝乳剂对小鼠急性毒性与遗传毒性研究

目的探究贯筋藤作为凝乳剂的安全性。方法以贯筋藤凝乳剂为受试物,采用限量法评价贯筋藤凝乳剂对小鼠的急性毒性,观察小鼠体重及解剖后心脏、脾脏、肝脏、肾脏是否发生病变;采用Ames实验评价其遗传毒性,设20、100、500、2500、12500μg/皿剂量组进行遗传毒性实验。结果急性毒性实验未出现小鼠死亡情况。研究发现小鼠的精神亢奋,有较强攻击欲望,根据小鼠的生理状况对小鼠进行脑组织中MAO-A和5-HT含量检测发现空白组和实验组差异极显著(P0.01)。通过Ames试验检测贯筋藤凝乳剂的遗传毒性,根据结果发现在12500μg/皿的情况下出现抑制微生物生长的情况,而在2500μg/皿的剂量下统计每组菌落数差异不著性(P0.05)。结论贯筋藤凝乳剂的LD50剂量大于10000 mg/kg,并根据Ames试验确定贯筋藤凝乳剂在最高2500μg/皿不具有致突变作用。     

强变形工艺对2519A铝合金组织与性能的影响

通过硬度测试、拉伸性能测试、透射电镜观察等分析手段研究了不同强变形工艺下2519A铝合金的力学性能与微观组织。结果表明,经50%的冷轧变形和165 ℃人工时效后,2519A合金的力学性能明显提高,其抗拉强度、屈服强度和伸长率分别为522 MPa、468 MPa和8.5%。而在冷变形前添加165 ℃×2 h预时效处理,合金的力学性能进一步提高,其抗拉强度、屈服强度和伸长率分别达到535 MPa、497 MPa和8%。预时效处理可以提高合金中θ′相的密度,使析出相分布更加均匀,有助于提高合金的力学性能。     

退火工艺对激光选区熔化成形Ti6Al4V合金组织及室温力学性能的影响

以Ti6Al4V球形粉末为原料,利用激光选区熔化成形方法制备了Ti6Al4V合金试样,采用光学显微镜、扫描电镜及力学性能测试等手段,研究了退火工艺对Ti6Al4V合金室温力学性能及组织的影响规律。结果表明: SLM成形沉积态Ti6Al4V合金室温抗拉强度超过1200 MPa,而平均断后伸长率仅为4.0%;在650 ℃下进行真空退火处理,合金的抗拉强度仍保持在1200 MPa左右,规定塑性延伸强度R_p0.2高于1150 MPa,但试样的断后伸长率<10%;而在750及800 ℃下进行真空退火处理,合金试样的抗拉强度降至1100 MPa左右,规定塑性延伸强度高于1050 MPa,伸长率达到甚至超过10%,材料的综合强韧性得到明显提升。随着真空退火加热温度和保温时间的增加,SLM成形Ti6Al4V合金原始β晶界逐渐变模糊,晶粒趋向于等轴化。与此同时,快速冷却转变的α′针状马氏体未出现明显地粗化。     

直流配网型混合动力系统在车客渡船上的应用

针对车客渡船动力负荷切换频繁,传统柴油机推进油耗高、排放和噪声大的问题,提出一种基于变速发电机组和超级电容储能装置的直流配网型混合动力系统。目前系统已成功应用于“江苏路渡3011”轮,实船运行数据显示:该混合动力系统不仅能达到较好的节油效果、降低排放,更在操控性、舒适性上优于传统的柴油机推进模式。