Surface roughness evolutions in sliding wear process

Wear debris analysis is a technique for machine condition monitoring and fault diagnosis. One key issue that affects the application of wear debris analysis for machine condition monitoring is whether the morphology of the wear particles accurately depicts their original states and the surface morphology of the components from which the particles separate. This study aimed to investigate the evolution of the surface morphology of wear debris in relation to change in the surface morphology of wear components in sliding wear process. Sliding wear tests were conducted using a ball-on-disc tester under proper lubrication and improper lubrication conditions. The study of the particle size distribution and the surfaces of both the wear debris and the tested samples in relation to the wear condition and the wear rates of the wear components were carried out in this study. The evolutions of the surface topographies of both the wear debris and the wear components as wear progressed were investigated. This study has provided insight to the progress of material degradation through the study of wear debris. The results of this research have clearly demonstrated that: (a) there is a good correlation of the surface morphology of wear debris and that of the wear components, and (b) the surface morphology of wear debris contains valuable information for machine condition monitoring.     

Wear-Debris Analysis in Expert Systems

The strategies of monitoring a machine's condition and carrying out operational equipment maintenance are changing, driven by the need for greater economic and time efficiency in modern industry. Nowadays, wear-debris analysis is becoming one of the key techniques used to screen for the presence of abnormal wear in many types of industrial machinery. It is required by industry to have a cost-effective and efficient wear-particle analysis technique to monitor the condition of a machine reliably. The project described here sought to develop a computerised smart system; that is, an expert system to interpret comprehensive data obtained from particle analysis, in order to assess wear modes and wear rates of gearboxes. This paper presents the development procedure, functions and advantages of the expert system.     

Automatic wear‐particle classification using neural networks

Although the study of wear debris can yield much information on the wear processes operating in machinery, the method has not been widely applied in industry. The main reason is that the technique is currently time consuming and costly due to the lack of automatic wear particle analysis and identification techniques. In this paper, six common types of metallic wear particles have been investigated by studying three‐dimensional images obtained from laser scanning confocal microscopy. Using selected numerical parameters, which can characterise boundary morphology and surface topology of the wear particles, two neural network systems, i.e., a fuzzy Kohonen neural network and a multi‐layer perceptron with backpropagation learning rule, have been trained to classify the wear particles. The study has shown that neural networks have the potential for dealing with classification tasks and can perform wear‐particle classification satisfactorily. This revised version was published online in June 2006 with corrections to the Cover Date.     

The development of ferrography in China—some personal reflections

Ferrography was first introduced into China over 20 years ago. Since then, academic research on wear debris formation mechanisms, intelligent identification of wear particle and other aspects have been carried out. Successful application of ferrography to machine condition monitoring in industry has been achieved with attendant cost benefits. Academic research has accelerated the development of ferrography, in which British scientists have made a considerable contribution to it. The future of ferrography is encouraging.     

A study of information technology used in oil monitoring

Oil monitoring is referred to as wear particle debris analysis and physical analysis of lubricants' properties. Information technology, such as database technology, image processing, expert system, data fusion and multi-agent system theories, etc. makes oil-monitoring procedures more intelligent. This paper introduces the development of some information systems such as management database, computer-aided wear particle analysis software used in oil monitoring. The basic principles and models of information technologies used in this field are presented. The development of IT as an integral element of an oil analysis-based condition monitoring system has been carried out in the authors' laboratory since 1986. Initially, the principal application of IT was to improve the data management and processing procedures. The second use of IT was in the development of a wear particle image processing system. The third aspect of utilizing IT is artificial intelligence. Finally, the application of IT-based network techniques is implemented. The progress in the application of IT in the authors' laboratory shows that the combination of information technology and oil monitoring can increase the speed of oil analysis, manage the information conveniently and obtain analysis conclusion more precisely in relation to practical application.     

Correlation between the tribological behaviour and wear particle morphology—case of grey cast iron 250 versus Graphite and PTFE

Wear particles are produced as a result of interaction of two surfaces in mechanical systems. After extracting and separating particles by different techniques (filtration, ferrography …), their morphology, which is a function of the condition of tribological system (tribosystem), can be related directly to the wear process. In order to understand wear mechanisms, it is interesting to study the relationship between the characterization of wear particles (aspect ratio, shape factor, spike parameter) and the mechanical factors of wear. Therefore, a series of tests were conducted with polytetrafluoroethylene (PTFE) versus cast iron, and flake graphite versus cast iron. Wear particles were studied in a particular tribosystem to understand debris formation and wear. Consequently, after friction tests according to a plane on plane contact, wear particles were collected. After separation tests, particle morphology obtained through image analysis techniques, was used to determine how debris are produced and to elucidate wear mechanisms. Finally, we propose a wear mechanism with these particular tribosystems.     

Condition monitoring of steam turbine-generator through contamination analysis of used lubricating oil

The objective of this paper is to present a systematic analysis of wear particles contained in used lubricant of steam turbine-generator of a thermal power station. The turbogenerator was condition-monitored over a period of two years through wear debris and particulate contamination analysis of the oil. Various sophisticated techniques such as automatic particle counter, ferrography, inductively coupled plasma atomic emission spectrometry (ICPAES), scanning electron microscopy and energy dispersive X-ray analysis (SEM/EDAX) have been employed to extract the relevant information about the health of the machine. Eventually, a correlation of different techniques of wear debris monitoring on the basis of current investigation ascertains the significance of the collective approach of various techniques to avoid catastrophic breakdowns and expensive component replacements.     

基于铁谱分析的颗粒分类识别方法与应用

铁谱颗粒分析是机器磨损状态监测与维修决策制定最有效的油液分析方法。通过近年来开展工业企业机器油液监测积累的大量铁谱磨粒图像,进行基于不同的颗粒特征的分类识别探究,并基于不同颗粒形成机制与原因提出切合工业现场的润滑管理维保策略。应用实践表明,铁谱分析方法在机器磨损状态监测、润滑磨损诊断机制判别以及企业润滑管理提升活动中仍发挥着积极作用。     

The interpretation of data from wear debris analysis, with particular reference to ferrography

A theoretical model of an oil lubrication system is presented. Wear particle generation and loss and lubricant usage are taken into account. The model can be used to reveal changes in the instantaneous wear rate from measurements of the wear debris concentration. The example of a diesel engine run-in is given. The model is also used to highlight the differences between oil analysis techniques which result from their differing sensitivities to particle sizes.     

The characterization of wear transitions in sliding wear process contaminated with silica and iron powder

When a machine is in operation, two moving surfaces interact to generate a large amount of wear particles. The wear debris generated inside the machine or contaminants from outside plays important roles in both two-body and three-body wear. For all mining and port machinery, their lubricants are very likely to be polluted by contaminants such as silica and other metallic debris such as iron and nickel. In order to seek a deeper understanding of the effects of different contaminants on wear process, this project investigated sliding wear processes when silica powder and iron powder exist in lubricants.Four sliding wear tests were conducted on a pin-on-disc tester with and without the contaminants. Visual inspection, ferrography analysis, particle quantity analysis using a particle analyzer, and numerical surface analysis using confocal laser scanning microscopy (CLSM) were conducted to study the wear particles and wear surfaces. Supported by the data generated from the comprehensive analyses on the wear particles and wear surfaces, the investigation of the effects of the added contaminants to the wear processes and wear mechanisms have been carried out and presented in this paper.     

SYCLOPS—a qualitative debris classification system developed for RAF early failure detection centres

Wear debris analysis of material originating from aircraft lubrication systems is one of the main stays of the Royal Air Force equipment health-monitoring programme. Although this is an effective tool in the assessment of equipment condition, it is labour intensive and relies heavily upon the experience of the operator. For the past 10 years there has been an ongoing Ministry of Defence sponsored research programme to develop WDA procedures for application within RAF Early Failure Detection Centres. This paper summarises this work to date and describes how this has resulted in the successful development of the SYCLOPS wear debris characterisation package.     

A New On-Line Visual Ferrograph

An on-line visual ferrograph (OLVF) characterized by direct reading and on-line analysis was developed based on magnetic deposition and image analysis. A digital sensor was integrated with a CMOS image sensor to obtain images of deposited wear debris under illumination conditions. An electromagnetic instrument was designed to deposit the wear debris flowing through an oil flow channel. The oil flow channel, fixed on the electromagnet, was arranged parallel to the magnetic flux in the air gap between two electromagnet poles. The deposition effect on wear debris was analyzed theoretically. The result shows that the wear debris in different sizes can be deposited in the same zone by controlling the oil flow rate and magnet field intensity. Corresponding application software for image sampling and processing was developed. An index of relative wear debris concentration, IPCA (Index of Particle Coverage Area), is given as an output in addition to wear debris images. Finally, two kinds of experiments were specified to assess the effect and validity of the OLVF. The results show that the OLVF has effective deposition and identification for both relatively large and small wear debris with rational control parameters. The validity examinations with the commercial particle quantifier (PQ) and direct reading ferrograph (DR) show that the OLVF has an approaching trend to the reference instruments in both heavily and lightly contaminated oil.     

Automated classification of wear particles based on their surface texture and shape features

In this study, the automated classification system, developed previously by the authors, was used to classify wear particles. Three kinds of wear particles, fatigue, abrasive and adhesive, were classified. The fatigue wear particles were generated using an FZG back-to-back gear test rig. A pin-on-disk tribometer was used to generate the abrasive and adhesive wear particles. Scanning electron microscope (SEM) images of wear particles were acquired, forming a database for further analysis. The particle images were divided into three groups or classes, each class representing a different wear mechanism. Each particle class was first examined visually. Next, area, perimeter, convexity and elongation parameters were determined for each class using image analysis software and the parameters were statistically analysed. Each particle class was then assessed using the automated classification system, based on particle surface texture. The results of the automated particle classification were compared to both the visual assessment of particle morphology and the numerical parameter values. The results showed that the texture-based classification system was a more efficient and accurate way of distinguishing between various wear particles than classification based on size and shape of wear particles. It seems that the texture-based classification method developed has great potential to become a very useful tool in the machine condition monitoring industry.     

Intelligent identification of wear mechanism via on-line ferrograph images

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.     

Quantitative methods in wear debris analysis

Wear debris has been recovered from several test systems and analyzed using different methods. These methods produced specific information concerning the particulate size and composition. A magnetic debris recovery method was quantitatively evaluated using debris samples and also using collections of manufactured particulates having known sizes and compositions. Small 5 μm diameter SiO₂ spheres, some containing nickel, were used to simulate debris. Other particulates of iron and nickel in different size ranges were also used in order to investigate such matters as size resolution, lubricant dilution techniques, particle overlap difficulties and the general problem of calibration of debris recovery systems. A comparison between chemical analysis and particulate analysis findings is presented. The application of optical and electron microscope methods and X-ray microanalysis in characterizing the wear particulates was carried out directly on the recovery substrate; these techniques are described.     

Watershed-Based Morphological Separation of Wear Debris Chains for On-Line Ferrograph Analysis

Separation and characterization of wear debris from ferrograph images are demanded for on-line analysis. However, particle overlapping issue associated with wear debris chains has markedly limited this technique due to the difficulty in effectively segmenting individual particles from the chains. To solve this bottleneck problem, studies were conducted in this paper to establish a practical method for wear debris separation for on-line analysis. Two conventional watershed approaches were attempted. Accordingly, distance-based transformation had a problem with oversegmentation, which led to overcounting of wear debris. Another method, by integrating the ultimate corrosion and condition expansion (UCCE), introduced boundary-offset errors that unavoidably affected the boundary identification between particles, while varying the corrosion scales and adopting a low-pass filtering method improved the UCCE with satisfactory results. Finally, together with a termination criterion, an automatic identification process was applied with real on-line wear debris images sampled from a mineral scraper gearbox. With the satisfactory separation result, several parameters for characterization were extracted and some statistics were constructed to obtain an overall evaluation of existing particles. The proposed method shows a promising prospect in on-line wear monitoring with deep insight into wear mechanism.