科创中国

As one of the most wear monitoring indicator, dimensional feature of individual particles has been studied mostly focusing on off-line analytical ferrograph. Recent development in on-line wear monitoring with wear debris images shows that merely wear debris concentration has been extracted from on-line ferrograph images. It remains a bottleneck of obtaining the dimension of on-line particles due to the low resolution, high contamination and particle's chain pattern of an on-line image sample. In this work, statistical dimension of wear debris in on-line ferrograph images is investigated. A two-step procedure is proposed as follows. First, an on-line ferrograph image is decomposed into four component images with different frequencies. By doing this, the size of each component image is reduced by one fourth, which will increase the efficiency of subsequent processing. The low-frequency image is used for extracting the area of wear debris, and the high-frequency image is adopted for extracting contour. Second, a statistical equivalent circle dimension is constructed by equaling the overall wear debris in the image into equivalent circles referring to the extracted total area and premeter of overall wear debris. The equivalent circle dimension, reflecting the statistical dimension of larger wear debris in an on-line image, is verified by manual measurement. Consequently, two preliminary applications are carried out in gasoline engine bench tests of durability and running-in. Evidently, the equivalent circle dimension, together with the previously developed concentration index, index of particle coverage area(IPCA), show good performances in characterizing engine wear conditions. The proposed dimensional indicator provides a new statistical feature of on-line wear particles for on-line wear monitoring. The new dimensional feature conveys profound information about wear severity.

Condition based maintenance(CBM) issues a new challenge of real-time monitoring for machine health maintenance. Wear state monitoring becomes the bottle-neck of CBM due to the lack of on-line information acquiring means. The wear mechanism judgment with characteristic wear debris has been widely adopted in off-line wear analysis; however, on-line wear mechanism characterization remains a big problem. In this paper, the wear mechanism identification via on-line ferrograph images is studied. To obtain isolated wear debris in an on-line ferrograph image, the deposition mechanism of wear debris in on-line ferrograph sensor is studied. The study result shows wear debris chain is the main morphology due to local magnetic field around the deposited wear debris. Accordingly, an improved sampling route for on-line wear debris deposition is designed with focus on the self-adjustment deposition time. As a result, isolated wear debris can be obtained in an on-line image, which facilitates the feature extraction of characteristic wear debris. By referring to the knowledge of analytical ferrograph, four dimensionless morphological features, including equivalent dimension, length-width ratio, shape factor, and contour fractal dimension of characteristic wear debris are extracted for distinguishing four typical wear mechanisms including normal, cutting, fatigue, and severe sliding wear. Furthermore, a feed-forward neural network is adopted to construct an automatic wear mechanism identification model. By training with the samples from analytical ferrograph, the model might identify some typical characteristic wear debris in an on-line ferrograph image. This paper performs a meaningful exploratory for on-line wear mechanism analysis, and the obtained results will provide a feasible way for on-line wear state monitoring.

Wear is a major factor of disc cutters' failure. No current theory offers a standard for the prediction of disc cutter wear yet. In the field the wear prediction method commonly used is based on the excavation length of tunnel boring machine(TBM) to predict the disc cutter wear and its wear law, considering the location number of each disc cutter on the cutterhead(radius for installation); in theory, there is a prediction method of using arc wear coefficient. However, the preceding two methods have their own errors, with their accuracy being 40% or so and largely relying on the technicians' experience. Therefore, radial wear coefficient, axial wear coefficient and trajectory wear coefficient are defined on the basis of the operating characteristics of TBM. With reference to the installation and characteristics of disc cutters, those coefficients are modified according to penetration, which gives rise to the presentation of comprehensive axial wear coefficient, comprehensive radial wear coefficient and comprehensive trajectory wear coefficient. Calculation and determination of wear coefficients are made with consideration of data from a segment of TBM project(excavation length 173 m). The resulting wear coefficient values, after modification, are adopted to predict the disc cutter wear in the follow-up segment of the TBM project(excavation length of 5621 m). The prediction results show that the disc cutter wear predicted with comprehensive radial wear coefficient and comprehensive trajectory wear coefficient are not only accurate(accuracy 16.12%) but also highly congruous, whereas there is a larger deviation in the prediction with comprehensive axial wear coefficient(accuracy 41%, which is in agreement with the prediction of disc cutters' life in the field). This paper puts forth a new method concerning prediction of life span and wear of TBM disc cutters as well as timing for replacing disc cutters.

Due to the large variations of environment with ever-changing background and vehicles with different shapes, colors and appearances, to implement a real-time on-board vehicle recognition system with high adaptability, efficiency and robustness in complicated environments, remains challenging. This paper introduces a simultaneous detection and tracking framework for robust on-board vehicle recognition based on monocular vision technology. The framework utilizes a novel layered machine learning and particle filter to build a multi-vehicle detection and tracking system. In the vehicle detection stage, a layered machine learning method is presented, which combines coarse-search and fine-search to obtain the target using the Ada Boost-based training algorithm. The pavement segmentation method based on characteristic similarity is proposed to estimate the most likely pavement area. Efficiency and accuracy are enhanced by restricting vehicle detection within the downsized area of pavement. In vehicle tracking stage, a multi-objective tracking algorithm based on target state management and particle filter is proposed. The proposed system is evaluated by roadway video captured in a variety of traffics, illumination, and weather conditions. The evaluating results show that, under conditions of proper illumination and clear vehicle appearance, the proposed system achieves 91.2% detection rate and 2.6% false detection rate. Experiments compared to typical algorithms show that, the presented algorithm reduces the false detection rate nearly by half at the cost of decreasing 2.7%–8.6% detection rate. This paper proposes a multi-vehicle detection and tracking system, which is promising for implementation in an on-board vehicle recognition system with high precision, strong robustness and low computational cost.

Forming limit curves(FLCs) are commonly used for evaluating the formability of sheet metals. However, it is difficult to obtain the FLCs with desirable accuracy by experiments due to that the friction effects are non-negligible under warm/hot stamping conditions. To investigate the experimental errors, experiments for obtaining the FLCs of the AA5754 are conducted at 250℃. Then, FE models are created and validated on the basis of experimental results. A number of FE simulations are carried out for FLC test-pieces and punches with different geometry configurations and varying friction coefficients between the test-piece and the punch. The errors for all the test conditions are predicted and analyzed. Particular attention of error analysis is paid to two special cases, namely, the biaxial FLC test and the uniaxial FLC test. The failure location and the variation of the error with respect to the friction coefficient are studied as well. The results obtained from the FLC tests and the above analyses show that, for the biaxial tension state, the friction coefficient should be controlled within 0.15 to avoid significant shifting of the necking location away from the center of the punch; for the uniaxial tension state, the friction coefficient should be controlled within 0.1 to guarantee the validity of the data collected from FLC tests. The conclusions summarized are beneficial for obtaining accurate FLCs under warm/hot stamping conditions.

The active magnetic bearing(AMB)suspends the rotating shaft and maintains it in levitated position by applying controlled electromagnetic forces on the rotor in radial and axial directions.Although the development of various control methods is rapid,PID control strategy is still the most widely used control strategy in many applications,including AMBs.In order to tune PID controller,a particle swarm optimization(PSO)method is applied.Therefore,a comparative analysis of particle swarm optimization(PSO)algorithms is carried out,where two PSO algorithms,namely(1)PSO with linearly decreasing inertia weight(LDW-PSO),and(2)PSO algorithm with constriction factor approach(CFA-PSO),are independently tested for different PID structures.The computer simulations are carried out with the aim of minimizing the objective function defined as the integral of time multiplied by the absolute value of error(ITAE).In order to validate the performance of the analyzed PSO algorithms,one-axis and two-axis radial rotor/active magnetic bearing systems are examined.The results show that PSO algorithms are effective and easily implemented methods,providing stable convergence and good computational efficiency of different PID structures for the rotor/AMB systems.Moreover,the PSO algorithms prove to be easily used for controller tuning in case of both SISO and MIMO system,which consider the system delay and the interference among the horizontal and vertical rotor axes.

Current researches show that mechanical deformation of seal ring face makes fluid film clearance decrease at high pressure side, thus a divergent clearance is formed and face wear occurs more seriously at the high pressure side than that on the low pressure side. However, there is still lack of published experimental works enough to prove the theoretical results. In this paper, a spiral groove dry gas seal at high pressures is experimentally investigated so as to prove the face wear happened at the high pressure side of seal faces due to the face mechanical deformation, and the wear behavior affected by seal ring structure is also studied. The experimental results show that face wear would occur at the high pressure side of seal faces due to the deformation, thus the leakage and face temperature increase, which all satisfies the theoretical predictions. When sealed pressure is not less than 5 MPa, the pressure can provide enough opening force to separate the seal faces. The seal ring sizes have obvious influence on face wear. Face wear, leakage and face temperature of a dry gas seal with the smaller cross sectional area of seal ring are less than that of a dry gas seal with bigger one, and the difference of leakage rate between these two sizes of seal face width is in the range of 24%–25%. Compared with the effect of seal ring sizes, the effect of secondary O-ring seal position on face deformation and face wear is less. The differences between these two types of dry gas seals with different secondary O-ring seal positions are less than 5.9% when the rotational speed varies from 0 to 600 r/min. By linking face wear and sealing performance changes to the shift in mechanical deformation of seal ring, this research presents an important experimental method to study face deformation of a dry gas seal at high pressures.

Cyber physical systems(CPS)recently emerge as a new technology which can provide promising approaches to demand side management(DSM),an important capability in industrial power systems.Meanwhile,the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics.DSM,integrated with CPS,is an effective methodology for solving energy optimization problems in manufacturing center.This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems.To gain prior knowledge of DSM operating results,a sparse Bayesian learning based componential forecasting method is introduced to predict24-hour electric load levels for specific industrial areas in China.From this data,a pricing strategy is designed based on short-term load forecasting results.To minimize total energy costs while guaranteeing manufacturing center service quality,an adaptive demand side energy optimization algorithm is presented.The proposed scheme is tested in a machining center energy optimization experiment.An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center.Based on the data collected from the sensing system,the load prediction-based energy optimization scheme is implemented.By employing both the PSO and the CPSO method,the problem of DSM in the manufacturing center is solved.The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5%compared with the traditional PSO optimization method.

Tribological properties of impregnated graphite are greatly influenced by preparation technology and working conditions and it's highly susceptible to corrosion environmental impacts,but the experimental research about it are few.In this paper,three kinds of impregnated graphite samples are prepared with different degree of graphitization,the tribological properties of these samples in the dry friction environment and in a corrosive environment are analyzed and contrasted.The tribo-test results show that the friction coefficient of samples is reduced and the amount of wear of samples increase when the graphitization degree of samples increases in dry friction condition.While in a corrosive environment(samples are soaked N2O4),the friction coefficient and amount of wear are changed little if the graphitization degree of samples are low.If the degree of graphitization increase,the friction coefficient and amount of wear of samples increase too,the amount of wear is 2 to 3 times as the samples tested in the non-corrosive environment under pv value of 30MPa?m/s.The impregnated graphite,which friction coefficient is stable and graphitization degree is in mid level,such#2,is more appropriate to have a work in the corrosion conditions.In this paper,preparation and tribological properties especially in corrosive environment of the impregnated graphite is studied,the research conclusion can provide an experimental and theoretical basis for the selection and process improvement of graphite materials,and also provide some important design parameters for contact seal works in a corrosive environment.

Joining of aluminum to steel has attracted significant attention from the welding research community,automotive and rail transportation industries.Many current welding methods have been developed and applied,however,they can not precisely control the heat input to work-piece,they are high costs,low efficiency and consist lots of complex welding devices,and the generated intermetallic compound layer in weld bead interface is thicker.A novel pulsed double electrode gas metal arc welding(Pulsed DE-GMAW)method is developed.To achieve a stable welding process for joining of aluminum to steel,a mathematical model of coupled arc is established,and a new control scheme that uses the average feedback arc voltage of main loop to adjust the wire feed speed to control coupled arc length is proposed and developed.Then,the impulse control simulation of coupled arc length,wire feed speed and wire extension is conducted to demonstrate the mathematical model and predict the stability of welding process by changing the distance of contact tip to work-piece(CTWD).To prove the proposed PSO based PID control scheme's feasibility,the rapid prototyping experimental system is setup and the bead-on-plate control experiments are conducted to join aluminum to steel.The impulse control simulation shows that the established model can accurately represent the variation of coupled arc length,wire feed speed and the average main arc voltage when the welding process is disturbed,and the developed controller has a faster response and adjustment,only runs about 0.1 s.The captured electric signals show the main arc voltage gradually closes to the supposed arc voltage by adjusting the wire feed speed in 0.8 s.The obtained typical current waveform demonstrates that the main current can be reduced by controlling the bypass current under maintaining a relative large total current.The control experiment proves the accuracy of proposed model and feasibility of new control scheme further.The beautiful and smooth weld beads are also obtained by this method.Pulsed DE-GMAW can thus be considered as an alternative method for low cost,high efficiency joining of aluminum to steel.