Zhongxiao Peng

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.

An integrated approach to fault diagnosis of machinery using wear debris and vibration analysis

Vibration and wear debris analyses are the two main condition monitoring techniques for machinery maintenance and fault diagnosis. In practice, these two techniques are usually conducted independently, and can only diagnose about 30–40% of faults when used separately. However, recent evidence shows that combining these two techniques provides greater and more reliable information, thereby resulting in a more effective maintenance program with large cost benefits to industry. In this paper, the correlation of vibration analysis and wear debris analysis was investigated. An experimental test rig consisting of a worm gearbox driven by an electric motor was set up to examine the correlation of the two techniques under various wear conditions. Three tests were conducted under the following conditions: (a) lack of proper lubrication, (b) normal operation, and (c) with the presence of contaminant particles added to the lubricating oil. Oil samples and vibration data were collected regularly. Wear debris analysis included the study of particle number and size distribution, the examination of particle morphology and types to determine possible wear mechanisms, and the analysis of chemical compositions to assess wear sources. Fault detection in the vibration signature was compared with the particle analysis. The results from this paper have given more understanding on the dependent and independent roles of vibration and wear debris analyses in machine condition monitoring and fault diagnosis.

Expert system development for vibration analysis in machine condition monitoring

Expert systems can be adapted for machine condition monitoring data interpretation due to the ability to identify systematic reasoning processes. As vibration analysis in condition monitoring is still generally performed by highly trained professionals, the use of expert systems would allow a greater analysis throughput as well as enabling technicians to perform routine analysis. The development of an expert system for vibration analysis of fixed plant is discussed, as well as laboratory and industry testing. Unique to existing developments, the expert system incorporates triaxial and demodulated frequency and time domain vibration data analysis algorithms for high accuracy fault detection. The tests confirm the potential value of the expert system for both laboratory and on-site maintenance departments of large manufacturing and mineral processing plants.

Biomimetic characterisation of key surface parameters for the development of fouling resistant materials.

Material science provides a direct route to developing a new generation of non-toxic, surface effect-based antifouling technologies with applications ranging from biomedical science to marine transport. The surface topography of materials directly affects fouling resistance and fouling removal, the two key mechanisms for antifouling technologies. However, the field is hindered by the lack of quantified surface characteristics to guide the development of new antifouling materials. Using a biomimetic approach, key surface parameters are defined and quantified and correlated with fouling resistance and fouling removal from the shells of marine molluscs. Laser scanning confocal microscopy was used to acquire images for quantitative surface characterisation using three-dimensional surface parameters, and field assays correlated these with fouling resistance and fouling release. Principle component analysis produced a major component (explaining 54% of total variation between shell surfaces) that correlated with fouling resistance. The five surface parameters positively correlated to increased fouling resistance were, in order of importance, low fractal dimension, high skewness of both the roughness and waviness profiles, higher values of isotropy and lower values of mean surface roughness. The second component (accounting for 20% of variation between shells) positively correlated to fouling release, for which higher values of mean waviness almost exclusively dictated this relationship. This study provides quantified surface parameters to guide the development of new materials with surface properties that confer fouling resistance and release.

Wear particle classification in a fuzzy grey system

The analysis and identification of wear particles for machine condition monitoring is usually conducted by experienced inspectors, and, thus, the process is usually very time-consuming. To overcome this obstacle, grey system theory has been applied in this study. The theory of grey systems is a new technique to perform prediction, relational analysis and decision making in many areas. In this paper, the theory of grey relational grades has been used to classify six types of metallic wear debris whose three-dimensional images are acquired from laser scanning confocal microscopy. Their boundary morphology and surface topology are then described by certain numerical parameters. Since the parameters have different levels of significance for different types of wear debris for particle identification, weighting factors of the parameters have been taken into consideration. To determine the weighting factors for the study, fuzzy logic has been applied. This study has demonstrated that a grey system combined with fuzzy logic can be used to classify wear particles satisfactorily.

The development of three-dimensional imaging techniques of wear particle analysis

Wear particles are produced whenever moving surfaces interact. The analysis of wear particles is now widely applied for non-intrusive examination of the condition of machinery, and can provide useful information for machine maintenance. There have been many attempts to automate the analysis of wear particles in recent years, as visual analysis is often time consuming, expensive and inconsistent. In this study, laser scanning confocal microscopy (LSCM) has been further developed and applied to obtain three-dimensional images of wear particles. LSCM includes a transmission sensor to acquire transmission and reflected (confocal) images simultaneously. The transmission sensor can provide far better images for boundary analysis, therefore, appropriate images containing both boundary and surface data are available. Software for the image analysis of boundaries and surfaces has been developed in this study.

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.

Computer image analysis of wear particles in three-dimensions for machine condition monitoring

Six common types of metallic wear particles, i.e., cutting, spherical, rubbing, laminar, fatigue chunk and severe sliding particles, have been studied in this paper based on the features of their boundary morphology and surface topography. Certain numerical descriptors, such as area, fibre ratio, height aspect ratio and fractal dimension, have been chosen to describe the characteristics of the boundary profiles of the wear debris. Since boundary parameters are insufficient to distinguish certain types of wear debris, several surface parameters extracted from the amplitude and spatial properties of the particles have been applied to investigate the surface roughnesses and textures of laminar, fatigue chunk and severe sliding wear debris. The study shows that classifying wear debris by studying the combined properties of their boundary profiles, surface roughnesses and textures can identify these six types of wear particles. Furthermore, nine parameters have been selected as criteria for wear particle analysis, and can be used to develop automatic computer wear particle analysis systems based on numerical descriptors.

Two-dimensional fast Fourier transform and power spectrum for wear particle analysis

Abstract Statistical parameters, such as R a and R q , have been widely used to investigate the roughness of wear particle surfaces in the literature. It has been reported that wear particle analysis based only on numerical characterization is often insufficient to distinguish certain types of wear debris. In this study, two-dimensional fast Fourier transform, power spectrum and angular spectrum analyses are applied to describe wear particle surface textures in three dimensions. Laminar, fatigue chunk and severe sliding wear particles, which have previously proven difficult to identify by statistical characterization, have been studied. The results show that spectral analysis effectively identifies the surface texture pattern (e.g. isotropy or anisotropy) and can be applied to classify these three types of wear particles.

An integrated intelligence system for wear debris analysis

Abstract Wear debris generated from two moving surfaces inside a machine is a direct wear product of operating machinery. The study of the debris can reveal wear mechanisms, wear modes and wear phases undergoing in the machine. Hence, wear debris analysis can be a very useful means to assess the condition of the machine. However, the current techniques for individual particle analysis are usually time-consuming and costly due to the requirement of analyst’s expertise to perform particle inspection, morphology characterisation and data interpretation. The limitation has obstructed the wide application of this method. Therefore, it is necessary to develop effective, reliable and cost-efficient techniques to perform wear debris analysis for industrial application. This paper presents a fully computerised package for wear debris analysis. The package includes three major systems corresponding to a three-dimensional particle analysis system, an automatic particle identification system and an expert system, communicating with each other through user-friendly interfaces. The successful development of such a system has demonstrated the possibility to achieve a fully computerised analysis system for routine and in-depth wear debris study for machine condition monitoring and fault diagnosis.