Chengqing Yuan

The use of the fractal description to characterize engineering surfaces and wear particles

Fractals can be extremely useful when applied to tribology. Obtaining fractal descriptions of engineering surfaces and wear particles requires surface topography information to be measured, digitized and processed. Such procedures can be rigorous. This article compares various methods to calculate profile and surface fractal dimension. Profile fractal dimension is computed using three available methods, corresponding to the yard-stick, the power spectrum and the structure function method. The precision of the three methods is analyzed and compared in this paper. Surface fractal dimension is calculated using the slit island and the box counting method. Both profile fractal dimension and surface fractal dimension are used to describe TiN coating surfaces and wear particles.

Surface Characterization Using Wavelet Theory and Confocal Laser Scanning Microscopy

Surface characterization, particularly roughness analysis, is very important for a wide range of applications including wear assessment. This paper proposes a set of methods and techniques to acquire appropriate images using confocal laser scanning microscopy, to separate roughness, waviness, and form using wavelet theory, and to characterize surface roughness for engineering surfaces and surfaces of small particles. Two application examples on engineering surfaces and wear particles have been presented in the paper to demonstrate that the method developed in this study can be used to measure surface roughness reliably and precisely. A guide on how to determine the iris size, step size, and objective lens has been scientifically provided according to theoretical analysis and experimental results.

Tribological properties of AlCoCrFeNiCu high-entropy alloy in hydrogen peroxide solution and in oil lubricant

The tribological properties of AlCoCrFeNiCu high entropy alloy sliding against GCr15 in hydrogen peroxide with different concentrations were evaluated using a ring-on-block wear test machine.The microstructure and morphology of the worn surfaces were analyzed using scanning electron microscopy(SEM) and the surface components of the tested samples were measured by energy dispersive spectrometer(EDS).The results indicated that the friction coefficient of AlCoCrFeNiCu/GCr15 tended to decrease with increasing concentration of hydrogen peroxide.The wear volume of AlCoCrFeNiCu alloy in hydrogen peroxide was much smaller than that in deionized water.The main wear mechanism was adhesive wear in deionized water,while the friction pair was dominated by a mixture of oxidative wear,abrasive wear and adhesive wear in 30% and 60% H2O2.In contrast with that in other concentrations,the wear resistance of AlCoCrFeNiCu alloy in 90% H2O2 increased significantly due to the formation of a compact oxide film.

Study on Influence of Cylinder Liner Surface Texture on Lubrication Performance for Cylinder Liner–Piston Ring Components

A marine diesel engine, where the cylinder liner–piston ring (CLPR) pair is one of the most important rubbing pairs, is the heart of a marine system. Studying the lubrication characteristics of the CLPR will provide a guide for rational design of the CLPR to reduce wear and prolong its service life. The surface texture features have a significant impact on the lubricating performance of the CLPR. In this study, the tribological system of the CLPR was investigated. Different surface textures (such as different sizes of surface concaves and grooves, etc.) were designed and produced on the cylinder liners using surface treatment. A series of experimental tests were then carried out in a specially designed diesel engine tester to investigate the tribological characteristics of the treated CLPR pairs. The comparison analyses of the worn surface texture features, element content of the lubrication oil, and abrasive particle characteristics were conducted under different wear surface texture features and cylinder liner speeds. The analysis results showed that there were significant differences in the tribological and lubrication properties of the rubbing pairs in different wear surface texture features. The wear performance of the CLPR pair with a regular concave texture was superior to that of the concave and groove, and regular groove textures. In addition, the regular concave with a depth-diameter ratio of 0.1 was the most effective surface texture to improve the lubrication and wear properties of the CLPR pairs. It is believed that the knowledge obtained in this study provides the real practical basis for tribological design and manufacturing of CLPR pair in marine diesel engines.

A fault diagnosis approach for gears using multidimensional features and intelligent classifier

Gear mechanisms are an important element in a variety of industrial applications and about 80% of the breakdowns of the transmission machinery are caused by the gear failure. Efficient incipient fault detection and accurate fault diagnosis are therefore critical to machinery normal operation. A new hybrid intelligent diagnosis method is proposed in this work to identify multiple categories of gear defection. In this method, wavelet packet transform (WPT), empirical mode decomposition (EMD) and Wigner-Ville distributions (WVD), combined with autoregressive (AR) model algorithm, were performed on gear vibration signals to extract useful fault characteristic information. Then, multidimensional feature sets including energy distribution, statistical features and AR parameters were obtained to represent gear operation conditions from different perspectives. The nonlinear dimensionality reduction algorithm, i.e. isometric mapping (Isomap), was employed in statistics to mine the intrinsic structure of the feature space in a low-dimensional space, and thus to speed up the training of the probabilistic neural network (PNN) classifier and enhance its diagnosis accuracy. Experiments with different gear faults were conducted, and the vibration signals were measured under different drive speeds and loads. The analysis results indicate that the proposed method is feasible and effective in the gear multi-fault diagnosis, and the isolation of different gear conditions, including normal, single crack, compound fault of wear and spalling, etc., has been accomplished. Since the recognition results are available directly from the output of PNN, the proposed diagnosis technique provides the possibility to fulfill the automatic recognition on gear multiple faults

Study on wear behaviour and wear model of nitrile butadiene rubber under water lubricated conditions

Nitrile Butadiene Rubber (NBR) is widely used to make water-lubricated rubber stern tube bearings in the marine field. Its tribological properties, which significantly influence its reliable life, directly affect the safe navigation, covert performance and operating costs of a ship. This study aimed to investigate the tribological properties and wear model of NBR under water-lubricated conditions. A CBZ-1 tribo-tester was used to conduct sliding wear tests between NBR pins and 1Cr18Ni9Ti stainless steel discs under water-lubricated conditions. The surface morphologies of the worn NBR pins were examined using laser-interference profilometry and scanning electron microscopy. In addition, the friction coefficients, ageing times and wear rates were analysed and compared to study the tribological properties of NBR and to identify the factors that affect its wear mass loss. The results demonstrated that different ageing times, velocities and loads had a significant effect on the friction and wear properties of the NBR specimens. The ageing times positively correlated with the friction coefficients and the wear mass losses between the rubbing pairs. The anti-tear properties of NBR deteriorated when the material was aged at a high temperature for an extended period of time, which reduced its wear-resistance. The main wear mechanism between the rubbing pairs was severe adhesion tearing wear under the water-lubricated conditions. A comprehensive empirical model for its wear rate estimation was established based on the wear and friction power. The model revealed the relationships between wear and velocity, as well as load and shore hardness. The result produced by the model was largely consistent with the experimental results. The knowledge gained in this study is anticipated to provide the theoretical data for a wear theory study of NBR and be useful for the optimisation of water-lubricated rubber stern tube bearings.

A new method of nonlinear feature extraction for multi-fault diagnosis of rotor systems

Rotor systems have been extensively used in a variety of industrial applications. However an unexpected failure may cause a break down of the rotational machinery, resulting in production and significant economic losses. Efficient incipient fault diagnosis is therefore critical to the machinery normal operation. Noise and vibration analysis is popular and effective for the rotor fault diagnosis. One of the key procedures in the fault diagnosis is feature extraction and selection. Literature review indicates that only limited research considered the nonlinear property of the feature space by the use of manifold learning algorithms in the field of mechanical fault diagnosis, and nonlinear feature extraction for rotor multi-fault detection has not been studied. This paper reports a new development based on a novel supervised manifold learning algorithm (adaptive locally linear embedding) applied to nonlinear feature extraction for rotor multiple defects identification. The adaptive locally linear embedding (ALLE) combines with the adaptive nearest neighbour algorithm and supervised locally linear embedding (LLE) to provide an adaptive supervised learning. Hence, distinct nonlinear features could be extracted from high-dimensional dataset effectively. Based on ALLE, a new fault diagnosis approach has been proposed. The independent component analysis (ICA) was firstly employed to separate the faulty components of the rotor vibration from the observation data. Then wavelet transform (WT) was used to decompose the recovered signals, and statistical features of frequency bands were hence calculated. Lastly, ALLE was applied to learn the low-dimensional intrinsic structure of the original feature space. The experiments on vibration data of single and coupled rotor faults have demonstrated that sensitive fault features can be extracted efficiently after the ICA-WT-ALLE processing, and the proposed diagnostic system is effective for the multi-fault identification of the rotor system. Furthermore, the proposed method achieves higher performance in terms of the classification rate than other feature extraction methods such as principal component analysis (PCA) and locally linear embedding (LLE).

Tribological Properties of Water-lubricated Rubber Materials after Modification by MoS2 Nanoparticles.

Frictional vibration and noise caused by water-lubricated rubber stern tube bearings, which are generated under extreme conditions, severely threaten underwater vehicles’ survivability and concealment performance. This study investigates the effect of flaky and spherical MoS2 nanoparticles on tribological properties and damping capacity of water-lubricated rubber materials, with the aim of decreasing frictional noise. A CBZ-1 tribo-tester was used to conduct the sliding tests between rubber ring-discs and ZCuSn10Zn2 ring-discs with water lubrication. These materials’ typical mechanical properties were analysed and compared. Coefficients of friction (COFs), wear rates, and surface morphologies were evaluated. Frictional noise and critical velocities of generating friction vibration were examined to corroborate above analysis. Results showed that spherical MoS2 nanoparticles enhanced rubber material’s mechanical and tribological properties and, in turn, reduced the friction noise and critical velocity. Flaky MoS2 nanoparticles reduced COF but did not enhance their mechanical properties, i.e., the damping capacity, wear resistance property; thus, these nanoparticles did not reduce the critical velocity obviously, even though increased the frictional noise at high load. The knowledge gained in the present work will be useful for optimizing friction pairs under extreme conditions to decrease frictional noise of water-lubricated rubber stern tube bearings.

Marine environmental damage effects of solar cell panel

Solar cell is one of the crucial components in photovoltaic systems. At present, substrate crystalline silicon solar cells with clear cover glasses are widely used in photovoltaic systems. The solar panels are made of semiconducting materials including mono crystalline silicon, polycrystalline silicon and gallium arsenide (GaAs). The high transmittance glass cover is pressed together with the panel through silicone rubber, which provides a strong protection for the core solar cells. A key issue of the photovoltaic system is the photovoltaic conversion efficiency, which not only depends on the conversion efficiency of the semiconducting silicon, but also is closely related to the transmittance of the cover glass. The photovoltaic conversion efficiency will definitely reduce with the attenuation of spectral transmittance. The rigorous environment where the solar cells are used in, such as in space radiation, desert and marine environments, will cause contamination, corrosion, wear and deterioration of the optical properties of the glass cover. The results in this study show that the spectral transmittance of the cover glass decreases with the increase of the submerging time in seawater. Therefore, it is necessary to study the attenuation of the optical properties of the cover glass and to characterise surface damage. Progress in research on environmental damage effect of solar panels has been reviewed and presented in this paper. The future direction of the research in this field has also been explored.

Gear faults diagnosis based on wavelet-AR model and PCA

Gear mechanisms are an important element in a variety of industrial applications and about 80% of the breakdowns of the transmission machinery are caused by the gear failure. Efficient incipient faults detection and accurate faults diagnosis are therefore critical to machinery normal operation. The use of mechanical vibration signals for fault diagnosis is significant and effective due to advances in the progress of digital signal processing techniques. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-faults diagnosis was presented in this paper based on the wavelet-Autoregressive (AR) model and Principal Component Analysis (PCA) method. The virtual prototype simulation and the experimental test were firstly carried out and the comparison results prove that the traditional Fast Fourier Transform Algorithm (FFT) analysis is not appropriate for the gear fault detection and identification. Then the wavelet-AR model was applied to extract the feature sets of the gear fault vibration data. In this procedure, the wavelet transform was used to decompose and de-noise the original signal to obtain fault signals, and the fault type information was extracted by the AR parameters. In order to eliminate the redundant fault features, the PCA was furthermore adopted to fuse the AR parameters into one characteristic to enhance the fault defection and identification. The experimental results indicate that the proposed method based on the wavelet-AR model and PCA is feasible and reliable in the gear multi-faults signal diagnosis, and the isolation of different gear conditions, including normal, single crack, single wear, compound fault of wear and spalling etc., has been effectively accomplished.