Research on information technology of state monitoring and fault prediction for mechatronics systemEI北大核心CSCD

The safety and reliability of mechatronics systems,particularly the high-end,large and key mechatronics equipment in service,can strongly influence on production efficiency,personnel safety,resources and environment.Based on the demands of development of modern industries and technologies such as international industry 4.0,Made-in-China 2025 and Internet + and so on,this paper started from revealing the regularity of evolution of running state of equipment and the methods of signal processing of low signal noise ratio,proposed the key information technology of state monitoring and earlyfault-warning for equipment,put forward the typical technical line and major technical content,introduced the application of the technology to realize modern predictive maintenance of equipment and introduced the development of relevant safety monitoring instruments.The technology will play an important role in ensuring the safety of equipment in service,preventing accidents and realizing scientific maintenance.     

Erratum to “Research on feature enhancement method of weak fault signal of rotating machinery based on adaptive stochastic resonance”

Journal of Mechanical Science and Technology -     

The application on order analysis for the rotating machinery with LabVIEWEI北大核心CSCD

Order analysis is regarded as one of the most significant method for monitoring and analyzing rotational machinery for the phenomenon of " frequency smear".However,the order analysis based on resampling is a signal processingwhich converts the constant time interval sampling into constant angle interval sampling,while with the variety of the rotational speed.The superiority of the order analysis is investigatedon implement of order analysis.Andthrough comparing the advantage and disadvantage between spectrum and order analysis,the paper will discuss the order analysis form a deep perspective.     

Sparse decomposition method based on time–frequency spectrum segmentation for fault signals in rotating machinery

The impulse signal in large rotating machinery with damage fault is sparse, weak, coupled, and even nonperiodic in intermittent operation. To extract this complex signal is a key topic in machinery fault diagnosis. Sparse decomposition (SD) has excellent adaptability in describing arbitrary complex signals based on over-complete dictionary. However, the pursuit speed of best atom is a serious drawback. To alleviate this, a method of sparse decomposition based on time–frequency spectrum segmentation (SD-TFSS) is introduced. Generalized S transform (GST) provides the capability to show the distribution of vibration signals, but the resolution is susceptible to noise, multiresolution generalized S-transform (MGST) is developed to generate multiresolution time–frequency spectrums. Then, spectrums fusion with an appropriate threshold is adopted to acquire multiresolution binary spectrums and produce an optimal binary spectrum. From this optimal binary spectrum, all the connectivity areas are extracted and marked by spectrum segmentation. Thus, an optimal library can be constructed by selecting the optimal atoms of every connectivity area, and the signal can be expressed with this library. We conduct simulations and experiments demonstrating that the proposed method performs well with lower pursuit complexity, higher decomposition efficiency, and better approximation precision.     

企业介绍北大核心CSCD

珠海格力电器股份有限公司成立于1991年,1996年11月在深交所挂牌上市。公司成立初期,主要依靠组装生产家用空调,现已发展成为多元化、科技型的全球工业制造集团,产业覆盖家用消费品和工业装备两大领域,产品远销180多个国家和地区。     

Gear fault identification based on Hilbert–Huang transform and SOM neural network

Gear vibration signals always display non-stationary behavior. HHT (Hilbert–Huang transform) is a method for adaptive analysis of non-linear and non-stationary signals, but it can only distinguish conspicuous faults. SOM (self-organizing feature map) neural network is a network learning with no instructors which has self-adaptive and self-learning features and can compensate for the disadvantage of HHT. This paper proposed a new gear fault identification method based on HHT and SOM neural network. Firstly, the frequency families of gear vibration signals were separated effectively by EMD (empirical mode decomposition). Then Hilbert spectrum and Hilbert marginal spectrum were obtained by Hilbert transform of IMFs (intrinsic mode functions). The amplitude changes of gear vibration signals along with time and frequency had been displayed respectively. After HHT, the energy percentage of the first six IMFs were chosen as input vectors of SOM neural network for fault classification. The analysis results showed that the fault features of these signals can be accurately extracted and distinguished with the proposed approach.     

Optimal configuration selection method for stiffness identification of serial manipulators based on the κf(A)−1 criterion

In real industrial environment, the stiffness identification accuracy of manipulators is affected by various measurement errors. However, research that deals with the inevitable error perturbation is scarce. The κ_F(A)⁻¹ criterion is adopted for measurement configuration selection to solve this problem according to the perturbation analysis and derivation of solutions to systems of stiffness identification equations. The optimal measurement configurations are finally obtained using the DETMAX optimization algorithm based on the deduced criterion, which is the main contribution of this work. Results illustrate that the optimal configurations optimized by the DETMAX algorithm based on the κ_F(A)⁻¹ criterion can better overcome the influence of measurement errors, improve the identification accuracy, and reduce the compensated position error and fluctuation compared with the other optimization algorithms. Furthermore, the proposed criterion can be applied to the stiffness identification of serial manipulators in a real industrial environment.

     

Simulation study on dynamics of cylindrical gear meshing with the evolution meshing stiffnessEI北大核心CSCD

Simulation study on the cylindrical gear meshing with the evolution gear meshing stiffness is being done for better understanding the dynamic characteristics of the kinematics.With consideration of damping,bearing clearance and gear backlash nonlinearity,the dynamic model is set up and computed in MATLAB.The analysis about the relationship between the kinematic responses and the meshing stiffness are carried out.And the results showed that as the gear mesh stiffness is changed from small to large,the performance of the system is changed from the harmonic stable periodic motion to with one times,two times,four times,ending chaos of the stability of the bifurcation.The research results would have theoretical guidance value for the fault diagnosis in engineering.     

英威腾现身慕尼黑北大核心CSCD

迄今为止全球规模最大、影响最深的太阳能专业展览会——德国慕尼黑国际太阳能技术博览会（IntersolarMunich2012）于6月13—15日在德国慕尼黑新展览中心举办。此次展会聚集了国际上所有业内知名企业,向参观者展示了全球光伏产业的最新产品和科研成果。     

Application of local wave time-frequency method in reciprocating mechanical fault diagnosis

To diagnose the reciprocating mechanical fault. We utilized local wave time-frequency approach. Firstly,we gave the principle. Secondly, the application of local wave time-frequency was given. Finally, we discussed its virtue in reciprocating mechanical fault diagnosis.     

一种电机运行监测技术北大核心CSCD

为了克服电机有线监测的不足,提出了一种融合RFID技术的无线监测方法。首先,设计了无线加速度传感器标签;其次,采用RFID测试仪对标签进行通信测试;最后,实验环境下对电机表面振动在线监测,实时得出电机表面振动加速度值。通过分析实验结果,得到了电机振动加速度值与状态的初步关系,验证了该方法的有效性。     

阀门专刊特约主编寄语北大核心CSCD

阀门是流体输送系统中的控制部件,具有截止、止回、换向、调节流量、减压稳压、溢流泄压、分流或汇流等功能,是涉及材料、结构、控制、传动、工艺等多学科交叉的典型机电一体化产品,广泛应用于石油、化工、冶金、发电、国防军工等各行各业中,是流程工业装置中必不可少的关键部件。我国阀门行业经过几十年的发展,行业自主创新能力明显提高:在核电领域,核1级稳压器喷雾阀、核2级大气排放阀等产品已实现国产化,主蒸汽隔离阀在“华龙一号”项目应用;在石化领域,国产化超低温固定球阀、延迟焦化高温球阀成功交付使用;在煤化工领域,粉煤气化工艺高压浓相粉煤控制阀已经完全国产化替代;在天然气领域,大口径全焊接固定球阀、输气管道调压装置多型关键阀门产品已在西气东输等项目中投入使用;在通用机械领域,可生产大约12类,3000多个型号、4000多个规格的产品,阀门产品已基本满足国内市场的需要,成套水平和成套能力逐步提高。     

利德华福年底忙不停北大核心CSCD

北京利德华福电气技术何限公司成立于1998年,是一家年销售收入10亿元,正以年均增长率超过50％高速发展,并且拥有核心自主知识产权的高新技术企业,在国内高压大功率变频调速自动控制产品及技术解决方案等领域占据领先地位。     

利德华福快讯两则北大核心CSCD

中国大唐集团公司淮北职业技术培训基地承办的“中国大唐集团公司2012年变频技术高级专业技术人才培训班”,于2012年6月8—18日开班,参加人员近120人。作为大唐集团的第一家变频器中标企业,利德华福已有逾百台变频器在大唐集团成功投运,约占大唐集团变频器使用数量的40％,利德华福应邀为大唐集团公司技术人员授课,这也是其第二次为大唐集团技术人员进行授课。本次培训对象为中国大唐集团公司所属企业从事变频设备管理人员、运行人员及检修人员。大唐集团作为公司的重要客户,利德华福对于这次培训非常重视,派出经验丰富的郭建梅老师为大唐集团技术人员进行培训,课程内容包括利德华福公司简介、高压变频器常识、Harvest系列高压变频调速系统介绍、     

汉江工具技术改造亮点纷呈北大核心CSCD

近日,汉江工具从德国引进2台数控滚刀铲磨床、2台数控剃齿刀磨床、1台数控测量中心,为汉江工具技术改造再增添新亮点。截至2011年11月底,2011年公司共引进高端进口数控设备9台,总投资近5000万元。     

新能源汽车电池热管理控制系统设计北大核心CSCD

为了提高新能源汽车电池续航能力,设计了一种基于域控制器的热管理系统。该系统包括底层域控制器、执行器、传感器及配套上位机。域控制器以S12ZVM作为主控芯片,MLX81325和MLX81315作为从控芯片,通过2台电子水泵和2台电子水阀控制冷却液来调节汽车电池温度。为了提高驱动电子水泵的永磁同步电机(PMSM)的同步精度,设计了一种将超前同步补偿与二阶全局快速终端滑膜控制相结合的二重交叉耦合同步控制算法。经过仿真分析与实验验证,该热管理系统能够通过不同的工作模式有效提升汽车电池热管理效率。     

山高刀具精彩亮相DMC2012北大核心CSCD

在DMC2012展会上。山高刀具向广大观众展现了一个注重服务理念与产品创新的国际刀具品牌。山高展台以其2012年新产品展示、优越的位置、简约明快的北欧设计风格吸引了大量的观众及参展商。     

西门子推出新型直流调速装置北大核心CSCD

西门子驱动技术集团在中国推出了适用于直流应用的调速装置Sinamics DC Master（DCM）,进一步完善了其传动产品系列。     

基础设施网络关联模型研究EI北大核心CSCD

随着社会与科技发展,基础设施网络之间的相互关联日趋复杂。为研究它们之间的关联关系对网络带来的影响,在对基础设施网络进行物理结构特性分析的基础上,提出一种基于网络图和泰森多边形的基础设施网络建模方法,在模型构建的基础上,研究对比网络独立运行和关联运行时效能的变化以及网络中不同节点故障对网络效能的影响。实验结果表明:故障在关联基础设施网络之间的传播比在独立网络中更为迅速,受故障影响,网络效能下降速度更快;网络中不同节点失效对网络效能影响程度不同,基础设施网络间的关联关系会使节点的重要性发生改变。这一结论为发现和识别网络中基础设施网络重要节点,加强重要节点的预防保护和故障发现后的应急策略提供了理论依据与决策支持。