Papers by Author: Yuan Kang

Abstract: In this paper, the effect of sprayed coating on the surface of carbon steel on friction and abrasion properties of oil seals which are rubbed by various anti-wear coating materials on is investigated experimentally, and compared with the uncoated AISI 52100 bearing steel. We used the block vs ring tester to explore the friction coefficient of hard surface friction of 5 commonly used rubber seal to 4 different coating layers of bearing steel under oil/no oil conditions. Four coating materials are used, which are Ni-Cr-B-Si alloy, Ni-Cr-WC alloy, ceramics, and ceramics. Five varieties of the oil seal material named HNBR, NBR, FKM, ACM, and SIL are subjected to wear tests for the measurements of friction and abrasion. The experimental results show that HNBR has better wear resistance and less friction, ceramics have higher friction and wear resistance than other coatings due to higher hardness. In terms of oil seal and sprayed coating, Ni-Cr-B-Si alloy and ceramic powder are more suitable for surface wear resistance, because of its hardness and wear resistance and the degree of damage to the oil seal are more excellent. Generally, the greater the wear resistance of the oil seal material, the greater its friction with the coating.

Abstract: Under the situation of increasingly stringent requirements for production speed and quality, gearboxes in many manufacturing equipment production lines lose final processing quality after long-term severe service.Machine fault prognosis is for experts or on-site maintenance personnel to use measurement equipment to capture signals and conduct diagnosis and maintenance based on personal experience. As technologies are advancing rapidly and equipment is evolving with increasing level of scale, automation and complexity, traditional diagnostic methods are no longer applicable. The article aims at vibration signal of gearbox to establish analysis and prognosis techniques. Principal components analysis is utilized to effectively capture the characteristic signal. Logistic regression and autoregressive moving average models are utilized to establish the correlation between characteristic signal and gearbox health status and performance trend model for on-site maintenance personnel to instantly understand gearbox health status and potential problems.In addition, Self-organizing map is utilized to establish fault identification model for maintenance personnel to significantly reduce troubleshooting time. In the article, gear-rotor experiment platform is used to assess the feasibility of gearbox performance analysis and prognosis techniques. It is expected to provide gearbox makers and users with understanding of gearbox health status and total solutions.

Abstract: The increased instantaneous speed in signal patterns which generated by the mechanical equipment are largely non-stationary. The signal features are averaged in correspondence with the length of analysis time, thus making it impossible to highlight the signal characteristics and caused the difficulties in identifying or diagnosing faults. In this paper, the wavelet order spectrum method using a combination of wavelet transform (WT) and speed frequency ordering. The feature order does not change with variations in speed, thus can effectively identify non-stationary faults in mechanical equipment. In addition, Principal Components Analysis (PCA) is used to extract the main features of the wavelet order spectrum and reduce the volume of data. This is combined with self-organizing maps (SOM) to devise an artificial intelligence method for fault diagnosis in non-stationary states. Lastly, the wavelet order spectrum method is verified by using a gear-rotor test platform that proofs the feasibility for the theory.

Abstract: In rotary machinery, the symptoms of vibration signals in the frequency domain have been used as inputs for neural networks and diagnosis results can be obtained by network computation. However, in gear or rolling bearing systems, it is difficult to extract symptoms from vibration signals in the frequency domain where shock vibration signals are present, and neural networks do not provide satisfactory diagnosis results without adequate training samples. Bayesian networks provide an effective approach for fault diagnosis in cases given uncertain knowledge and incomplete information. To classify the shock of vibration signals in the gear system, this study uses statistical factors of vibration signals. Based on these factors, the fault diagnosis is implemented by using Bayesian networks and the results of the two methods, namely, back-propagation neural networks and probabilistic neural network in gear train systems, are compared.

Abstract: An analysis for the effect of nanoparticles in lubricants on load capacity is performed to study a rectangular thrust pad hydrostatic bearing with a central recess. The closed-form solution of the bearing load is derived analytically and presented for nanofluids with interparticle interaction. Results reveal that in the presence of nanoparticles, the enhanced viscosity could result in an increase in bearing load; moreover, this increase dramatically increases as particle volume fraction and/or interparticle interaction increases. The effect of nanoparticles on the bearing load can be magnified by decreasing the bearing gap.

Abstract: In this paper, the mathematic model of the swashplate type variable displacement axial piston pump (VDAPP) is established. The VDAPP applied to an electro-hydraulic servo control system usually induces unstable performance, so that a servo controller is designed and analyzed to control the swashplate angular displacement and improve the stability and transient response of pump performance. The flow and pressure variations of pump with the proposed controller are investigated and analyzed by mathematic simulations and experiments. The simulation and experiment results show that the proposed controller can improve the stability of swashplate angular displacement, and easily applied to the energy saving hydraulic systems.

Abstract: The compensation of thermal deformation is the most significant for the accuracy of a machine tool. This study proposes an approach based on genetic algorithms (GA) to build the dynamic model of the prediction for thermal deformation of a machine tool. GA is used to optimize the prediction accuracy by using appropriate number and locations of temperature sensors, the model order and the time delay between temperatures and thermal deformation. The compared results show that the proposed approach can improve the accuracy of prediction results and better than other methods.