Trend Prediction Method Based on the Largest Lyapunov Exponent for Large Rotating Machine Equipments

In order to predict electromechanical equipments＇ nonlinear and non-stationary condition effectively, max Lyapunov exponent is introduced to the fault trend prediction of large rotating mechanical equipments based on chaos theory. The predict method of chaos time series and two methods of proposing f and F are dis- cussed. The arithmetic of max prediction time of chaos time series is provided. Aiming at the key part of large rotating mechanical equipments-bearing, used this prediction method the simulation experiment is carried out. The result shows that this method has excellent performance for condition trend prediction.

Machines and Testing Equipment

National Quality Supervision ＆ Inspection Center for Refractories Business scope： Selective examination for national quality supervision; Identification of production license; Arbitration inspection and technical achievements evaluation; Commodities inspection and otherquality inspections .

Hardware,Machines & Electrical Equipment From Wenzhou

Scissors and locks are famous products from Wenzhou. Now scissors from the city including multi-purpose, tailoring, tourist and special-purpose ones and exquisite fruit and artistic knives have entered the world market. At the

American Factories Continue to Increase Equipment Investment——Competitors in Asia also increased their consumption of machine tools.

去年美国金属加工工厂对新机床产品的需求量较 2004 年和 2003 年都有增加。消沉中的美国资本投资发生了一个转变,排在德国之前成为第三大消费国。然而对于美国机床业来说,不太好的消息是亚洲竞争对手同样增加了他们在机床的设备投资。来自第 41 次世界机床生产和消费的调查显示,中国、日本、韩国、印度以及中国台湾地区的机床消费都有显著增长。在 28 个国家和地区的调查中,四分之三的国家和地区消费都在增长,这同时也诠释了经济“表面消费”的含义。这一切表明美国的机床消费量还会继续保持增加,订购机器的数量会维持向上的势头,最新的数据也证明了这一点。此外,调查数据还罗列了其它国家,尤其是亚洲国家的机床生产情况(本刊2006 年第一期环球瞭望栏目文章图表中显示了世界主要机床生产状况):德国和日本领导着世界机床的生产;中国超过意大利成为第三大机床生产国;中国台湾地区排在第五位;美国机床的生产和出口排在台湾之后,保持小的增长;韩国与瑞士紧追其后;印度也毫不示弱。这28个被调查国家和地区的机床产量都比前一年平均增长了14% 。中国、日本、韩国、印度以及中国台湾地区占据了整个调查地区机床产量的 47.6% ,作为CECIMO 成员的由 15 个国家组成的西欧联盟占据了机床产量的 4 2 . 3 % 。

Navigating challenges and opportunities of machine learning in hydrogen catalysis and production processes: Beyond algorithm development

With the projected global surge in hydrogen demand, driven by increasing applications and the imperative for low-emission hydrogen, the integration of machine learning(ML) across the hydrogen energy value chain is a compelling avenue. This review uniquely focuses on harnessing the synergy between ML and computational modeling(CM) or optimization tools, as well as integrating multiple ML techniques with CM, for the synthesis of diverse hydrogen evolution reaction(HER) catalysts and various hydrogen production processes(HPPs). Furthermore, this review addresses a notable gap in the literature by offering insights, analyzing challenges, and identifying research prospects and opportunities for sustainable hydrogen production. While the literature reflects a promising landscape for ML applications in hydrogen energy domains, transitioning AI-based algorithms from controlled environments to real-world applications poses significant challenges. Hence, this comprehensive review delves into the technical,practical, and ethical considerations associated with the application of ML in HER catalyst development and HPP optimization. Overall, this review provides guidance for unlocking the transformative potential of ML in enhancing prediction efficiency and sustainability in the hydrogen production sector.

Design and Performance Analysis of Small Denture Machining Equipment

The research and application on small denture machining equipment are great breakthrough for modern dental restoration technology. In this paper, a small denture machining equipment made of two spindles with four-axis was designed based on machining characteristics and functional analysis. Position accuracy and re-position accuracy were measured by accuracy instrument. In order to test its machining capacity, some typical microstcucture parts, such as straight channel, hemispherical surface, and molars coronal, were selected for high speed milling. It was obtained that the denture machining equipment met the machining requirements with high quality and efficiency, according to the acquisition and analysis of form and position errors, surface roughness, and 3-D profile.

Kinetostatic Modeling and Analysis of an Exechon Parallel Kinematic Machine(PKM) Module

As a newly invented parallel kinematic machine（PKM）, Exechon has found its potential application in machining and assembling industries due to high rigidity and high dynamics. To guarantee the overall performance, the loading conditions and deflections of the key components must be revealed to provide basic mechanic data for component design. For this purpose, a kinetostatic model is proposed with substructure synthesis technique. The Exechon is divided into a platform subsystem, a fixed base subsystem and three limb subsystems according to its structure. By modeling the limb assemblage as a spatial beam constrained by two sets of lumped virtual springs representing the compliances of revolute joint, universal joint and spherical joint, the equilibrium equations of limb subsystems are derived with finite element method（FEM）. The equilibrium equations of the platform are derived with Newton＇s 2nd law. By introducing deformation compatibility conditions between the platform and limb, the governing equilibrium equations of the system are derived to formulate an analytical expression for system＇s deflections. The platform＇s elastic displacements and joint reactions caused by the gravity are investigated to show a strong position-dependency and axis-symmetry due to its kinematic and structure features. The proposed kinetostatic model is a trade-off between the accuracy of FEM and concision of analytical method, thus can predict the kinetostatics throughout the workspace in a quick and succinct manner. The proposed modeling methodology and kinetostatic analysis can be further expanded to other PKMs with necessary modifications, providing useful information for kinematic calibration as well as component strength calculations.

DYNAMICS ANALYSIS OF SPECIAL STRUCTURE OF MILLING-HEAD MACHINE TOOL

The milling-head machine tool is a sophisticated and high-quality machine tool of which the spindle system is made up of special multi-element structure. Two special mechanical configurations make the cutting performance of the machine tool decline. One is the milling head spindle supported on two sets of complex bearings. The mechanical dynamic rigidity of milling head structure is researched on designed digital prototype with finite element analysis（FEA） and modal synthesis analysis （ MSA ） for identifying the weak structures. The other is the ram structure hanging on milling head. The structure is researched to get dynamic performance on cutting at different ram extending positions. The analysis results on spindle and ram are used to improve the mechanical configurations and structure in design. The machine tool is built up with modified structure and gets better dynamic rigidity than it was before.

Machine Learning-Assisted High-Throughput Virtual Screening for On-Demand Customization of Advanced Energetic Materials

Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error.Herein,a methodology combining domain knowledge,a machine learning algorithm,and experiments is presented for accelerating the discovery of novel energetic materials.A high-throughput virtual screening(HTVS)system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established.With the proposed HTVS system,candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25112 molecules.Furthermore,a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results,thus verifying the effectiveness of the proposed methodology.This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.

Fault diagnosis for on-board equipment of train control system based on CNN and PSO-SVM hybrid model

Rapid and precise location of the faults of on-board equipment of train control system is a significant factor to ensure reliable train operation.Text data of the fault tracking table of on-board equipment are taken as samples,and an on-board equipment fault diagnosis model is designed based on the combination of convolutional neural network(CNN)and particle swarm optimization-support vector machines(PSO-SVM).Due to the characteristics of high dimensionality and sparseness of fault text data,CNN is used to achieve feature extraction.In order to decrease the influence of the imbalance of the fault sample data category on the classification accuracy,the PSO-SVM algorithm is introduced.The fully connected classification part of CNN is replaced by PSO-SVM,the extracted features are classified precisely,and the intelligent diagnosis of on-board equipment fault is implemented.According to the test analysis of the fault text data of on-board equipment recorded by a railway bureau and comparison with other models,the experimental results indicate that this model can obviously upgrade the evaluation indexes and can be used as an effective model for fault diagnosis for on-board equipment.

Synthesis of carbon dots with predictable photoluminescence by the aid of machine learning

Carbon dots(CDs)have wide application potentials in optoelectronic devices,biology,medicine,chemical sensors,and quantum techniques due to their excellent fluorescent properties.However,synthesis of CDs with controllable spectrum is challenging because of the diversity of the CD components and structures.In this report,machine learning(ML)algorithms were applied to help the synthesis of CDs with predictable photoluminescence(PL)under the excitation wavelengths of 365 and 532 nm.The combination of precursors was used as the variable.The PL peaks of the strongest intensity(λ_(s))and the longest wavelength(λ_(l))were used as target functions.Among six investigated ML models,the random forest(RF)model showed outstanding)performance in the prediction of the PL peaks.

Research and Application of Maintenance Decision Method of Complex Equipment in Nuclear Power Plant

The determination of maintenance mode of complex equipment in nuclear power plant is an essential work for reliability analysis and maintenance decision. Currently, the main decision method of maintenance mode is reliability centered maintenance( RCM) logic decision-making process, but the process is a qualitative analysis process. Based on a comprehensive analysis of factors affecting equipment reliability and maintenance work, it adopts a fuzzy synthesis decision method to establish a maintenance decision model,which uses the maximum subordination principle and expert assessment method to determine the maintenance mode of complex equipment. Combined with a concrete example of generators in nuclear power plant,a description of maintenance decision method was proposed in the application of complex equipment. The research shows that the method is feasible and reliable.

Progress in Chemical Synthesis of Peptides and Proteins

For the proteins that cannot be expressed exactly by cell expression technology (e.g., proteins with multiple posttranslational modifications or toxic proteins), chemical synthesis is an important substitute. Given the limited peptide length offered by solid-phase peptide synthesis invented by Professor Merrifield, peptide ligation plays a key role in long peptide or protein synthesis by ligating two small peptides to a long one. Moreover, high-molecular-weight proteins must be synthesized using two or more peptide ligation steps, and sequential peptide ligation is such an efficient way. In this paper, we reviewed the development of chemical protein synthesis, including solid-phase peptide synthesis, chemical ligation, and sequential chemical ligation. © 2017, The Author(s).

Towards Realizing Sign Language-to-Speech Conversion by Combining Deep Learning and Statistical Parametric Speech Synthesis

This paper realizes a sign language-to-speech conversion system to solve the communication problem between healthy people and speech disorders. 30 kinds of different static sign languages are firstly recognized by combining the support vector machine (SVM) with a restricted Boltzmann machine (RBM) based regulation and a feedback fine-tuning of the deep model. The text of sign language is then obtained from the recognition results. A context-dependent label is generated from the recognized text of sign language by a text analyzer. Meanwhile,a hiddenMarkov model (HMM) basedMandarin-Tibetan bilingual speech synthesis system is developed by using speaker adaptive training.The Mandarin speech or Tibetan speech is then naturally synthesized by using context-dependent label generated from the recognized sign language. Tests show that the static sign language recognition rate of the designed system achieves 93.6%. Subjective evaluation demonstrates that synthesized speech can get 4.0 of the mean opinion score (MOS).

Dimensional Synthesis Design of Novel Parallel Machine Tool

This paper presents dimensional synthesis design theory for a novel planar 3-DOF (degrees of freedom) parallel machine tool. Closed-form solutions are developed for both the inverse and direct kinematics. The formulation of the dexterity and the definitions of the theoretical workspace and the valid workspace are used to analyze the effects of the design parameters on the dexterity and workspace. The analysis results are used to propose an approach to satisfy the platform motion requirement while realizing orientation capability, dexterity and valid workspace. A design example is given to illustrate the effectiveness of this approach.

Pioneering integration of combinatorial chemistry and machine learning to accelerate the development of tailored LNPs for mRNA delivery

Ionizable lipid (IL), the most important component of lipid nanoparticles (LNPs) for messenger RNA (mRNA) delivery, is closely related to the mRNA expression and targeted property of LNPs. Recently, a pioneering research led by Li et al.1 published in Nature Materials reported a novel approach to the high-throughput screening of novel ILs for mRNA delivery via combinatorial chemistry and machine learning (ML) (Fig. 1). 119-23, a newly structured IL, was finally designed by this strategy, which outperformed commercial ILs (MC3 and SM102) in transfecting muscle cells and immune cells in several tissues. This study should accelerate the design and optimization of potent ILs, advancing the development of LNPs for efficient mRNA delivery.

Research on Microcrack Extension Mechanism of SiCp/Al in the Machining Process

The generation and development of microcracks of SiCp/Al materials in the machining process were researched, and the forming causes and mechanism of the cut surface morphology for ap=0.2 mm or ap=0.4 mm and at 2 m/min～10 m/min were systematically analyzed. The supporting and "floor"roles of aluminum are wake at a shallower cutting depth and a lower speed, the SiC particles are shed under the role of cutting force, and the microcrack size of cut surface is larger. With the increase in the cutting depth and the cutting speed, the cutting temperature increases, the supporting and "floor" roles of aluminum are enhanced, the shedding and fracture of the reinforcement particles are flexible under the action of the cutting force, the fracture of cutting transforms from brittle to ductile, and the expansion of microcrack on the entire surface tends to balance with a smaller size. A curve between the cutting force and the cutting speed was plotted, a microcrack form of the cut surface was given, and some theoretical basis is provided for the mechanical processing and the surface quality.

Machine learning-aided scoring of synthesis difficulties for designer chromosomes

Designer chromosomes are artificially synthesized chromosomes.Nowadays,these chromosomes have numerous applications ranging from medical research to the development of biofuels.However,some chromosome fragments can interfere with the chemical synthesis of designer chromosomes and eventually limit the widespread use of this technology.To address this issue,this study aimed to develop an interpretable machine learning framework to predict and quantify the synthesis difficulties of designer chromosomes in advance.Through the use of this framework,six key sequence features leading to synthesis difficulties were identified,and an e Xtreme Gradient Boosting model was established to integrate these features.The predictive model achieved high-quality performance with an AUC of 0.895 in cross-validation and an AUC of 0.885 on an independent test set.Based on these results,the synthesis difficulty index(S-index)was proposed as a means of scoring and interpreting synthesis difficulties of chromosomes from prokaryotes to eukaryotes.The findings of this study emphasize the significant variability in synthesis difficulties between chromosomes and demonstrate the potential of the proposed model to predict and mitigate these difficulties through the optimization of the synthesis process and genome rewriting.

Experimental Study on the Machining Technology of ZrO_2 Ceramics Using Diamond Wire Saw with Ultrasonic Vibration

The influence of different technological parameter on material remove rate and surface quality of ZrO2 ceramics is studied using the cutting machining method of electroplate diamond wire saw with ultrasonic vibration.Experimental results show that,compared with the same experiment condition without ultrasonic vibration,this cutting method has the advantages of high material remove rate,good surface quality,little brokenness and so on.

一种基于机器学习识别医疗设备异常运行状态方法的建立

由于传统识别医疗设备异常运行状态方法是通过手工预定义的关键字与设备响应数据的字段相匹配完成设备异常识别,易导致医疗设备识别结果不精确。基于此,本研究提出基于机器学习的医疗设备异常运行状态识别方法。基于机器学习的医疗设备异常运行状态识别方法通过传感器采集医疗设备的电压、电流、工作温度等异常特征数据,对多特征数据进行叠加融合,引入GRU网络构建机器学习识别模型,输入数据训练模型完成医疗设备异常运行状态识别。实验结果表明,基于机器学习的医疗设备异常运行状态识别方法可识别医疗设备不同类型的异常运行状态,误报率为4.43%,识别异常运行状态时间较短,准确度更高。