High-speed cam mechanism is widely used in vehicles, textile machinery, printing machinery etc. The research of dynamic analysis is the basis and key point to improve the mechanical properties. Discretization of the flexible elements of cam mechanism is carried out by means of finite-element method. Furthermore, the dynamic model of the rigid-flexible coupled cam mechanism is proposed based on modal synthesis technique and Kane’s equations. The dynamic equation obtained is more general for flexible beam type, and very convenient for solving by computer because it is a modal-coordinates dynamic equation. The system dynamic performance is studied by analyzing the system modal and solving the dynamic equation. The simulation of dynamics model which has different orders is carried on, which can not only ensure accuracy of calculation but also save computational resources. Research results have important guiding significance for determining whether the dynamic analysis of high-speed cam systems in practical application is required, and furthermore, research results can provide strong guide for dynamic modeling and modal characteristics research of similar mechanism.

Acoustical guided waves modal analysis in rail is the basis of long distance guided wave inspection and rail vibration and noises control. In particular the various propagating guided waves in the rail are identified in terms of their propagation wavenumber coefficients and their corresponding deformed shapes. The semi-analytical finite element method is used to formulate the governing equation for guided waves propagating in elastic waveguides of arbitrary cross-section on the basis of the virtual work principle, wavenumber dispersion curves and deformed shapes are extracted by solving governing equation, which provide the basis for selecting mode and frequency during application. The propagating guided waves wavenumber dispersion curves and deformed shaped under 0-8 kHz frequency range for CHN60 type free rail are obtained using semi-analytical finite element method, and the characteristics of the eight based guided waves mode in rail are discussed. Instrumented hammer experimental study and modal analysis using sever vibration accelerometers mounted on rail are carried out to verify the vertically vibrating mode and horizontally vibrating mode guided waves wavenumber dispersion coefficient. Modeling and experimental investigations of lateral and vertical excitation in railhead shows that good agreement between numerical calculation and experiment.

Robotics in medical have made giant strides in past several decades with its widely use in various aspects. According to it’s function and usage, medical robots are classified into 7 types: neurosurgery robot, orthopedics robot, laparoscopic robot, vascular interventional robot, prosthetics and exoskeleton robot, assistive and rehabilitation robot and capsule robot. The functions, specifications, advantages and disadvantages of present typical commercially available medical robotics are surveyed. The current research foci, key technology and future trends of medical robotics are also discussed.

Drill pipe is a critical and vulnerable component in oil & gas industry. A novel feed-through alternating current field measurement (ACFM) is proposed to achieve in-service detection for axial cracks on the surface of drill pipe. The finite element model is built by ANSYS software for analyzing the induced electromagnetic field distribution and disturbance caused by the axial crack on the external surface of drill pipe. Signal features of the crack are extracted through quantitatively analyzing the relationship between the size of crack and disturbance field. On the basis of theory and FEM simulation, the feed-through ACFM system is set up and proved by the axial crack of drill pipe detection experiments. The results show that, the signal features of Bx and Bz from the feed-through ACFM system show the depth and length information of the axial crack respectively on pipe string, and the lift-off effect can meet the demands of in-service detection for drill pipe. Furthermore, the feed-through ACFM system with sensors array can detect all the axial cracks in-service with a single pass scanning.

Since the concept of compliant mechanisms (CMs) is firstly proposed in late 1980s, it develops rapidly and has become an important branch of modern mechanism community. In the past less than 30 years, dozens of design methodologies and theories for CMs have emerged, which lays a solid foundation on their successful use. With the increasing insight for flexures and CMs, more and more applications of CMs can be found in various fields. After summarizing and comparing the current design methods for flexure-based compliant mechanisms five years ago, the state-of-arts of CMs have been overviewed from the point of application in this paper. Most existing CMs are resorted to three categories, i.e. precision engineering, bionic robotics, and smart structures if considering the difference of their functions and application background. The successful uses, study focuses and even application prospects in each category of CMs are introduced case by case. Afterward, four new types of CMs with great potential applications, i.e. cellular CMs, contact aid CMs, lamina emergent mechanisms, and static balance CMs are illustrated in brief. We hope more scholars all over the world start to study these amazing CMs and industrial personnel pay more attention on the usage of CMs as well after getting the overview of CMs.

In order to extract machinery fault characteristics that are submerged in strong background noise, a general singular value decomposition (SVD) based subspace noise reduction algorithm is applied to signal processing, i.e., μ-SVD based denoising method. It can be proved that the traditional SVD based denoising method is a special case of the μ-SVD based one where μ=0. μ-SVD based denoising method contains a filter factor that plays a role in restraining information contributions of the noise-domain singular values to the denoised signal. μ-SVD based denoising method involves five parameters, including delay time, embedding dimension, noise reduction order, noise power and Lagrange multiplier. The selection methods for these parameters are discussed. In particular, the effects of noise reduction order and Lagrange multiplier on denoising performance are also studied. The experimental results of simulation signal with local fault and vibration signal with early crack fault in gear demonstrate that the μ-SVD based denoising method is superior to the traditional one in denoising performance, and can more effectively extract the gear fault characteristics at the presence of strong background noise.

The major parameter estimation error source for discrete Fourier transform are spectrum leakage and picket fence effect. A weighted two points vector interpolation algorithm based on Rife-Vincent window for improving the estimation accuracy of parameters for the multi-frequency damped signals is presented, where the signals are weighted by M-order cosine window family before the discrete Fourier transform and the equations with nuknown of frequency deviation and decay factor is established by the ration of two adjacent spectral lines near the real frequency position. The frequencies, amplitudes and phases are calculated by the frequency deviation solved from the above equation. The cosine window family with the characteristic of maximum sidelobe decay can essentially remove the spectral leakage and two point vector interpolation will cope with the problem of the picket fence. To combine the merits of the above two method can effectively increase the accuracy. The simulation and test results show that the proposed algorithm has a higher estimate accuracy and steady estimates. The higher computational efficiency and lower memory demand are suggested especially for poor computing resource situations. It also can be used as an optional method for the features extraction of the multi-frequency damped signals.

Based on thermodynamics theory and finite element method, a finite element model of equipment and its airbag cushion system is established and verified experimentally. The simulation results are obtained under the same conditions of the airdrop test and the simulation results agree very well with the experimental results, which indicate the established model is valid for further research. Because nonlinear model of airbags cushion system is very complicated, the calculation takes tens of hours of CPU time. As a result, the large-scale calculation is impossible. In order to overcome this problem, surrogate models are employed instead of the complex finite element model based on extended latin hypercube method and radial basis function. Initial velocity, initial heeling angle, initial pitch angle, lateral velocity and gradient are variables, while maximum acceleration, maximum heeling angle, maximum pitch angle and maximum airbag pressure are responses. Considering the influence of landing condition, Monte Carlo method and surrogate model are used to calculate landing success probability of airdropping equipment under multi-condition. The landing success probability calculated is 95.84%. Acceleration is the primary contributor to cushion performance, while pressure inside airbag is secondary contributor.

Isogeometric analysis (IGA) is a novel numerical method for solving equations of physical fields, which is motivated by improving the integration between numerical simulations of physical phenomena and the computer-aided design tools, and provides a new way to associate product design with analysis and optimization via a common mathematical platform. The method of geometric modeling and its mathematical description are introduced in detail based on the computational framework of IGA. Taking NURBS as an example, the parametric representation in an analysis model and the mesh refinement method are analyzed, and the discretization methods, boundary conditions and quadrature in the parametric domain of IGA are discussed as well by comparing them with those in the standard finite element analysis. Applications of IGA in thermal analysis, fluid-structure interaction, contact problems and structural optimization are summarized and a simple example of 2D problem is provided.

A method for structural optimization design of wafer handling robot is presented. The static deformation of the end effector is employed as the constraint of the method. The nature frequencies of the rigid links flexible joints system are selected as the optimization objectives, and the thicknesses of the arms are determined as the optimization parameters. The dynamic model of the rigid links and flexible joints is established, and the modal analysis is carried out to determine the orders of the modal which will influence the trajectory precision. The masses of the second arm and the third arm are selected as the optimization parameters by the sensitive analysis of the nature frequencies. The deflection model of the arm is established with the variables of the thicknesses of the arm. The relationships between the thicknesses and the deformation of the end effector are analysed, and the structures of the arms are optimized based on the analysis. The comparison between the performance before optimization and the performance after optimization are carried out, and the result shows that the nature frequencies and the frequency of the vertical vibration are significantly enhanced and the deflection of the end effector is obviously reduced.

Recently, micro-nano coordinating measuring machine(CMM) has become a research focus in the field of three-dimensional(3D) micro-nano measurement. Due to the difficulty of the development of a 3D nano probe, a novel 3D resonant trigger probe has been proposed. This probe can reach nanometer/sub-nanometer resolution in three-dimensions, and its operation principle is different from the present contact probes and optical non-contact probes. The 3D nano resonant trigger probe is constructed by a piece of piezo-electrical PVDF film, two piezo-actuators, an integrated fiber micro-stem and micro-ball tip. The PVDF film vibrates at its resonant frequency and acts as a sensor. Using the piezoelectric property of PVDF film and the high sensitivity of its resonant parameters to the micro-force, the probe can give 3D trigger signal. The probe contacts the sample in traditional tapping-mode in z direction and friction-mode in both x and y directions. Experimental results show that the trigger resolution of 3D resonant trigger positioning system constructed by the above probe, 3D nano positioning unit, the feedback control module and the signal processing circuit could reach sub-nanometer resolution, which is 0.12 nm in x direction and 0.10 nm in y direction, while 0.12 nm in z direction. The 3D repeatability error is 26 nm, 36 nm and 10 nm respectively. The results demonstrate the validity of the new type of 3D nano resonant trigger probe and positioning system.

New energy vehicle(NEV) has been widely used around the world in response to the fossil energy crisis and environmental pollution problems. NEV will generate massive real-world data during its daily operating which is contributed by high electrification and intelligent networking. Applying these multi-source heterogeneous data for a security warning and technical analysis will play a key role in promoting the development of NEV industry in China. The current situation of data-driven analysis technology in the NEV field is reviewed. Firstly, the basic theory of big data analysis techniques are introduced and the development of big data technology is depicted. The structure and function of the National Monitoring and Management Platform for New Energy Vehicles are introduced, and the particular process of big data analysis on NEV is emphasized. The previous data-driven research and methods in power battery, NEV daily operation and charging behavior are proposed for discussion respectively. Some representative research results and applications are displayed at the same time. Finally, the issues and prospects of the data-driven method on NEV application field are summarized and forecasted.

Ultra-precision machining is essential method for obtaining the highest quality in terms of form accuracy, surface finish Ultra-precision machining is essential method for obtaining the highest quality in terms of form accuracy, surface finish and surface integrity. As the higher requirements of quality and diversifications for products are put forward, it is essential to improve the precision and efficiency of ultra-precision machining. Ultra-precision machining has become a complicated systems engineering, which involves more and more last research fruits. The conception, application fields, present research status, development tendency, and the key issues of future researches on scientific and technological f of ultra-precision machining are introduced in this paper. Some advanced and typical ultra-precision machine tools, ultra-precision cutting, grinding, and polishing are reviewed and compared in terms of machining accuracy, quality and efficiency. The main problems and miss distance from the advanced technologies of China in ultra-precision machining field are analyzed. In the same time, the probable further trend of ultra-precision machining is forecasted, and the developing countermeasure and strategy of fundamental research, technologies and industry in China are given.

With the development of science and technology as well as the advancement of production technology, contemporary equipment is increasingly developing towards large-scale, complex, automated and intelligent direction. In order to ensure the safety and reliability of equipment, the remaining useful life (RUL) prediction technology has received widespread attention and been widely used. Traditional statistical data-driven methods are obviously influenced by the choice of models. Machine learning has powerful data processing ability, and does not need exact physical models and prior knowledge of experts. Therefore, machine learning has a broad application prospect in the field of RUL prediction. In view of this, the RUL prediction methods based on machine learning are analyzed and expounded in detail. According to the depth of machine learning model structure, it is divided into shallow machine learning methods and deep learning methods. At the same time, the development branches and research status of each method are sorted out, and the corresponding advantages and disadvantages are summarized. Finally, the future research directions of RUL prediction methods based on machine learning are discussed.

Prognostics and health management (PHM) is crucial for ensuring the safe operation of machinery, improving the productivity and increasing economic benefits. High-quality life-cycle data, as the basic resource in the field of PHM, are able to carry the key information which reflects the complete degradation processes of machinery. However, due to the high costs in data acquisition and insufficient development in storage and transmission technology, typical life-cycle data is extremely scarce, which limits the theoretical research and engineering application of PHM for machinery. In order to solve this dilemma, accelerated life tests of rolling element bearings are carried out by Prof. Yaguo Lei's research group from School of Mechanical Engineering, Xi'an Jiaotong University (XJTU) and the Changxing Sumyoung Technology Co., Ltd. (SY), Zhejiang. These tests lasted for two years and the acquired datasets, i.e., XJTU-SY bearing datasets, have been publicly released for all PHM researchers. The XJTU-SY bearing datasets contain run-to-failure vibration signals of 15 rolling element bearings under three different operating conditions. These datasets have high sampling frequency, large amount of data, abundant failure types and detailed recording information. Accordingly, these datasets not only provide fresh "data blood" for PHM and promote the research of fault diagnosis and remaining useful life prediction, but also are able to help to improve intelligent maintenance decision making in industry.

The intelligent spindle possesses three major characteristics of processing state perception, decision-making and execution. It can realize self-feedback and self-control of machining status, which is the core component of intelligent manufacturing. It is the basic supporting technology for "Industrie 4.0" and "Made in China 2025" and the development direction for next-generation spindle technology. Condition monitoring, diagnosis and vibration control, as the typical embodiment of the three major functional features, are the core technologies for ensuring the efficient operation of intelligent spindles. Therefore, aiming at alleviating the problem of monitoring, diagnosis and vibration control of intelligent spindle, and with the monitoring, diagnosis and control of high-failure parts and failure modes as the clues, the current research status both at home and abroad is systematically summarized, the existing problems are concluded and the potential development trends are proposed.

As an important approach to realize MC and AM, modular product design can produce more variety of products with limited resources by combination of different modules. Such method is viewed as a goal of good design practice in current engineering. However, it is not received sufficient attention in the literature, and most solutions are proposed at an abstract level. The literature on modular product design focusing on definitions, methodologies, key technologies and its application is reviewed. Finally, some focused research issues on modular product design are formulated.

Bionic robots are good performance mechanical and electrical systems which imitate biological structures and motion characteristics of organisms according to the principles of bionics. They have shown their potential use in the dangerous conditions to human beings, such as anti-terrorism, space exploration and rescue. The bionic robots can be divided into three types according to their work conditions, such as the land bionic robots, the air bionic robots, and the underwater bionic robots. The developments of the bionic robots have experienced three stages, such as original exploration, imitation of the biological prototypes’ appearances and motions, mechanical and electrical systems with partial biological properties. The research status at home and abroad of these three types robots is discussed. And several problems of the researches are found after analysis, such as the lack of the biological motion mechanism researches, the traditional construction, material, driving and controlling mode, and the low efficiency of the power, leading to the difference between robots and creatures. These problems make the bionic robots similar in shape to the creatures but very different in essence. So the bionic robots now days are absence of effective applications. The development trend is pointed out that the bionic robots are developing towards lifelike system, including moving toward the rigid-flexible mechanism, integration of the bionics construction, material and driver, neuron fine control, and the efficient power transformation.

The core technology of the BigDog quadruped robot is analyzed. Adapting to the rough terrain is the main design clues of the BigDog. Improving horizontal and vertical degrees of freedom linkage ability is the main innovation of structure design. Not good motion characteristics, such as robot’s center of gravity ups and downs and self disturbance are the main reasons for being difficult to control. The components and advantage of the hydraulic power system are analyzed. Solving the driver problem of legged vehicles is the fundamental goal of the hydraulic system development. Supporting leg slipping or not, pitch and roll angle of the body too large or not are the main parameters as monitoring robot’s movement condition. IMU and joint encoder can detect the state parameters of the body and limbs. Terrain of foot placement can be restored by pressure sensor. Three-in-one can build a virtual model. By the virtual model, robot’s center of gravity and other key control process parameters can be calculated. At the same time, locomotion control system can do action drill roughly and accurate planning of kinematics or dynamics. The deviation of planning and prototype model is taken as the feedback for closed-loop control. LS3 constructs the navigation system of three-dimensional laser scanner and binocular vision as the main. LS3 can stride across rocky terrain by visual terrain reconstruction. Software system can integrate all the basic functions as an organic whole. Autonomy and intelligence of robot are discussed. BigDog/LS3 and Curiosity Mars Rover are compared and analyzed. BigDog has three big problems currently: instantaneously unable to increase hydraulic value significantly, all kinds of damage in mechanical transmission, bionic design not thoroughness. For the inadequacies of BigDog, several improvements are analyzed on the LS3. Petman, Cheetah and Wildcat robot are briefly analyzed. Atlas biped robot has crash protection function and can recovery equilibrium status quickly after external force hitting by virtual model.

Mechanical equipment in modern industries becomes more automatic, precise and efficient. To fully inspect its health conditions,condition monitoring systems are used to collect real-time data from the equipment, and massive data are acquired after the long-time operation, which promotes machinery health monitoring to enter the age of big data. Mechanical big data has the properties of large-volume, diversity and high-velocity. Effectively mining characteristics from such data and accurately identifying the machinery health conditions with advanced theories become new issues in machinery health monitoring. To harness the properties of mechanical big data and the advantages of deep learning theory, a health monitoring and fault diagnosis method for machinery is proposed. In the proposed method, deep neural networks with deep architectures are established to adaptively mine available fault characteristics and automatically identify machinery health conditions. Correspondingly, the proposed method overcomes two deficiencies of the traditional intelligent diagnosis methods: (1) the features are manually extracted relying on much prior knowledge about signal processing techniques and diagnostic expertise; (2) the used models have shallow architectures, limiting their capability in fault diagnosis issues. The proposed method is validated using datasets of multi-stage gear transmission systems, which contain massive data involving different health conditions under various operating conditions. The results show that the proposed method is able to not only adaptively mine available fault characteristics from the data, but also obtain higher identification accuracy than the existing methods.