The Experts below are selected from a list of 2277 Experts worldwide ranked by ideXlab platform
Zhongxiao Peng - One of the best experts on this subject based on the ideXlab platform.
-
Restoration of low-informative image for robust Debris shape measurement in on-line Wear Debris monitoring
Mechanical Systems and Signal Processing, 2019Co-Authors: Ngaiming Kwok, Yeping Peng, Zhongxiao PengAbstract:Abstract As a significant technique in machine condition monitoring, Wear Debris Analysis enables investigation of machine running condition with respect to Debris features including size, quantity and morphology. In particular, being capable of providing more comprehensive morphological information, three-dimensional Debris features are regarded essential and often acquired through a video-based Debris imaging process. However, Debris images captured often suffer degradation due to Debris motion blur and lubrication contamination, that hinder reliable Debris features extraction. To address the image degradation issue, a new method of Wear Debris image restoration is developed to reduce the effect of blur. In order to avoid the expensive computation involved in blind deconvolution methods, the Debris image was restored using localized boundary features. Based on the fact that Debris area and background area indicate distinctive brightness, a step edge model is applied to describe the original Debris boundary. Localized kernels on each side of Debris are then determined. Next, restorations are conducted with the estimated kernels to produce sharper Debris profiles with respect to different motion features. Final restoration is conducted by fusing the restored profiles according to the maximum local sharpness. Experimental results have demonstrated that this method allows reliable features extraction from blurred image, improving the robustness of video based Wear Debris Analysis.
-
Morphological Feature Extraction Based on Multiview Images for Wear Debris Analysis in On-line Fluid Monitoring
Tribology Transactions, 2016Co-Authors: Yeping Peng, Ngaiming Kwok, Shuo Wang, Chen Feng, Zhongxiao PengAbstract:ABSTRACTWear state is an important indicator of machinery operation condition that reveals whether faults have developed and maintenance should be scheduled. Among the available techniques, vision-based on-line monitoring of Wear particles in the lubricant circuit is preferred, where three-dimensional particle characterizations can be obtained for Wear mode Analysis. This article presents the application of an imaging system that captures Wear particles in lubricant flow and the development of image processing procedures for multiview feature extraction. In particular, a framework including background subtraction, object segmentation, and Debris tracking was adopted. Particle features were then used in a comprehensive morphological description of Wear Debris. Experiments showed that the system is able to produce a feasible and reliable indication of Wear Debris characteristics for machine condition monitoring.
-
AIM - Development of an expert system for automatic osteoarthritis diagnosis using numerical characterisations of articular cartilages and Wear particles
2013 IEEE ASME International Conference on Advanced Intelligent Mechatronics, 2013Co-Authors: Yuan Tian, Zhongxiao PengAbstract:As a common joint disease often caused by Wear and tear and particularly common for aged people, osteoarthritis (OA) occurs with articular cartilage deterioration and Wear particle generation. Current clinical OA diagnosis approaches are mainly based on qualitative evaluation of orthopaedists. This not only brings heavy cost to community healthcare, but can also limit the required service to OA patients in regional areas. In this paper, based on our previous work on the numerical Analysis of cartilage and Wear particles, an expert system has been established for automatic OA diagnosis using both cartilage and Wear particle Analysis methods. The developed system supported vector machine (SVM) to obtain cartilage and Wear particle data and applied a statistical classification method for an OA assessment. This was a first time that Wear particle Analysis technique was integrated into an OA diagnosis system. Internal evaluations showed that the correct OA degree recognition rates were 80% and 72% based on the cartilage and particle Analysis results, respectively. This paper presents the background information, how the system was developed, and the approach used to deal with inconsistent results from cartilage and Wear Debris Analysis. The proposed framework has demonstrated that it is feasible to develop an automatic and objective OA diagnosis system for future clinic applications.
-
Osteoarthritis diagnosis using Wear particle Analysis technique: Investigation of correlation between particle and cartilage surface in walking process
Wear, 2006Co-Authors: Zhongxiao PengAbstract:Osteoarthritis (OA), the most prevalent form of arthritis, is a degenerative joint disease affecting millions of people worldwide. The overall aims of the project were: (i) to gain a better understanding of the Wear processes occurring in human joints during a walking process, particularly in relation to osteoarthritic degeneration and (ii) to develop effective, minimally intrusive tools to assess OA activity and progression. Based on existing developments, this project, using three-dimensional numerical descriptors, further investigated correlation between the cartilage surface subjected and particle generated in the walking process. This is a critical step in developing and utilizing Wear particle Analysis techniques for OA assessment. With further study and development along with this study, it is possible to develop better diagnostic and prognostic procedures for clinic OA assessment using Wear Debris Analysis techniques.
-
The investigation of the condition and faults of a spur gearbox using vibration and Wear Debris Analysis techniques
Wear, 2005Co-Authors: Stephan Ebersbach, Zhongxiao Peng, N.j. KessissoglouAbstract:The aim of this work is to investigate the effectiveness of combining both vibration Analysis and Wear Debris Analysis in an integrated machine condition monitoring maintenance program. To this end, a series of studies was conducted on a spur gearbox test rig. After a test under normal condition was conducted to obtain the baseline information of the test rig, a number of different machine defect conditions were introduced under controlled operating conditions, corresponding to (i) constant overload conditions and (ii) cyclic load conditions. The data provided by Wear Debris Analysis was compared with vibration Analysis spectra in order to quantify the effectiveness of both vibration Analysis and Wear Debris Analysis in predicting and diagnosing machine failures. The paper discusses the use of a numerical approach for Wear Debris Analysis, facilitated by the use of a laser scanning confocal microscope (LSCM). The correlation between Wear Debris and vibration Analysis techniques is discussed, and the usefulness of the numerical descriptors for Wear Debris Analysis is evaluated.
N.j. Kessissoglou - One of the best experts on this subject based on the ideXlab platform.
-
The investigation of the condition and faults of a spur gearbox using vibration and Wear Debris Analysis techniques
Wear, 2005Co-Authors: Stephan Ebersbach, Zhongxiao Peng, N.j. KessissoglouAbstract:The aim of this work is to investigate the effectiveness of combining both vibration Analysis and Wear Debris Analysis in an integrated machine condition monitoring maintenance program. To this end, a series of studies was conducted on a spur gearbox test rig. After a test under normal condition was conducted to obtain the baseline information of the test rig, a number of different machine defect conditions were introduced under controlled operating conditions, corresponding to (i) constant overload conditions and (ii) cyclic load conditions. The data provided by Wear Debris Analysis was compared with vibration Analysis spectra in order to quantify the effectiveness of both vibration Analysis and Wear Debris Analysis in predicting and diagnosing machine failures. The paper discusses the use of a numerical approach for Wear Debris Analysis, facilitated by the use of a laser scanning confocal microscope (LSCM). The correlation between Wear Debris and vibration Analysis techniques is discussed, and the usefulness of the numerical descriptors for Wear Debris Analysis is evaluated.
-
A study of the effect of contaminant particles in lubricants using Wear Debris and vibration condition monitoring techniques
Wear, 2005Co-Authors: Z. Peng, N.j. Kessissoglou, M. CoxAbstract:Vibration and Wear Debris analyses are the two main conditions monitoring techniques for machinery maintenance and fault diagnosis. These two techniques have their unique advantages and disadvantages associated with the monitoring and fault diagnosis of machinery. When these techniques are conducted independently, only a portion of machine faults are typically diagnosed. However, practical experience has\ud shown that integrating these two techniques in a machine condition monitoring program provides greater and more reliable information, bringing significant cost benefits to industry. The objective of this work is to investigate the correlation between vibration Analysis and Wear Debris Analysis. This was achieved by investigating different operating conditions of an experimental rig, consisting of a worm gearbox driven by an electric motor. The worm gearbox was initially run under normal operating conditions as a comparative test. A series of tests were then conducted corresponding to lack of proper lubrication, and with different contaminant particles added to the various lubricants. Oil samples and vibration data were regularly\ud collected. Numerical data produced by Wear Debris Analysis were compared with vibration spectra, in order to quantify the effectiveness of the two condition monitoring techniques. The results from this paper have given more understanding on the dependent and independent roles\ud of vibration and Wear Debris analyses in predicting and diagnosing machine faults.\u
-
An integrated approach to fault diagnosis of machinery using Wear Debris and vibration Analysis
Wear, 2003Co-Authors: Zhongxiao Peng, N.j. KessissoglouAbstract: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.
-
integration of Wear Debris and vibration Analysis for machine condition monitoring
2003Co-Authors: Zhongxiao Peng, N.j. KessissoglouAbstract:In this paper, the correlation of vibration Analysis and Wear Debris Analysis for machine condition monitoring is both qualitatively and quantitatively investigated. Various Wear conditions were generated, using an experimental test rig consisting of a spur gearbox driven by an electric motor in order to examine the correlation of the two techniques. Two tests were conducted under the following two controlled operating conditions corresponding to constant normal load and constant overload. 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, Wear rates and 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.
-
The correlation of Wear Debris Analysis with vibrations Analysis of a worm gear
2002Co-Authors: Zhongxiao Peng, N.j. KessissoglouAbstract:Condition monitoring is a predictive maintenance technique to maximise the lifetime of machines, such as gearboxes, fans, pumps and electric motors. Vibration and oil analyses are the two main condition monitoring techniques for machinery maintenance and fault diagnosis. However, used independently, they can only diagnose less than 50% of faults. the objective of this proposal is to conduct an experimental and theoretical study on the correlation of vibration and Wear Debris analyses to result in an improved maintenance program. Successful integration of these two maintenance techniques will provide a huge economica benefit to industries such as mines, refineries and sugar mills.
Akshay Panchity - One of the best experts on this subject based on the ideXlab platform.
-
Wear Debris Analysis of Case Carburized & Mild Steel Debris in Single Stage Spur Gear Box
2013Co-Authors: Vijay Kumar Karma, Akshay PanchityAbstract:In this paper, various experiments were conducted to predict the behavior and effects of Wear Debris being dragged into the single stage spur gear mesh. For performing these experiments an experimental setup was fabricated using maruti 800 gear box. Debris of different weights of hard and soft materials, namely Case Carburized Steel and Mild Steel were taken. These are then introduced in the gear mesh at different speeds. To establish a relationship between mass of the Wear Debris introduced and the torque, the Wear Debris were then introduced into the gear mesh at different conditions. Torque loss value for a constant speed for a fixed mass of Wear Debris was then calculated by recording output torque value.
-
Wear Debris Analysis of case carburized mild steel Debris in single stage spur gear box
2013Co-Authors: Vijay Kumar Karma, Akshay PanchityAbstract:In this paper, various experiments were conducted to predict the behavior and effects of Wear Debris being dragged into the single stage spur gear mesh. For performing these experiments an experimental setup was fabricated using maruti 800 gear box. Debris of different weights of hard and soft materials, namely Case Carburized Steel and Mild Steel were taken. These are then introduced in the gear mesh at different speeds. To establish a relationship between mass of the Wear Debris introduced and the torque, the Wear Debris were then introduced into the gear mesh at different conditions. Torque loss value for a constant speed for a fixed mass of Wear Debris was then calculated by recording output torque value.
X.p. Yan - One of the best experts on this subject based on the ideXlab platform.
-
Effects of Fe powder on sliding Wear processes under lubricants with different viscosities
Proceedings of the Institution of Mechanical Engineers Part J: Journal of Engineering Tribology, 2008Co-Authors: C.q. Yuan, X.p. Yan, X.c. ZhouAbstract:Four sliding Wear tests were conducted using a ball-on-disc tester at room temperature to investigate sliding Wear processes in which Fe powders were added to the lubricant. The ball sample was a standard E52100 ball of 8 mm in diameter. The disc samples were made of 5140 steel and heat-treated to a hardness of 52 HRC. Wear Debris Analysis and surface Analysis of the tested samples were carried out to investigate the Wear particles generated from the Wear process and to study the effects of the soft Fe powder. It was proved that Fe powders in the lubricant played an important role in the Wear process and had a significant effect on the main Wear mechanisms, resulting in Wear-out failure.
-
Surface roughness evolutions in sliding Wear process
Wear, 2008Co-Authors: C.q. Yuan, Z. Peng, X.p. Yan, X.c. ZhouAbstract: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\ud 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\ud morphology of Wear Debris contains valuable information for machine condition monitoring
-
The study of sliding and rolling Wear using Wear Debris Analysis techniques
2005Co-Authors: Chengqing Yuan, Zhongxiao Peng, X.c. Zhou, X.p. YanAbstract:A large amount of research work has been conducted to evaluate and predict Wear condition of components in machinery using Wear Debris Analysis. However, there are few studies on quantitative surface characterization of Wear particles although it has been recognized as an objective and effective means for assessing Wear mechanisms and monitoring the condition of machinery. Moreover, for the surfaces of Wear components that are difficult to access or analyze, it is possible to evaluate the quantitative surface information of the Wear components through studying the surface characterization of corresponding Wear particles. Therefore, it is imperative that the study of the quantitative surface morphology of the Wear Debris is conducted. In this project, two most common motions occurring in machinery, that is, sliding and rolling, were simulated using a ball-on-disc tester. Wear particles were collected in running-in and steady-state Wear stages and analysed using ferrography, a particle analyser, confocal laser scanning microscopy and computer image Analysis techniques. Surface roughness parameters w;ere used to study the evolutions of the surface morphology of Wear particles from running-in to steady state Wear stages. Quantitative data on the evolutions of the surfaces of the Wear Debris were provided. The knowledge obtained in this research is important for predicting Wear conditions using Wear Debris Analysis techniques while further understanding of the Wear mechanisms and Wear characteristics has been also gained.
-
The surface roughness evolutions of Wear particles and Wear components under lubricated rolling Wear condition
Wear, 2005Co-Authors: C.q. Yuan, Z. Peng, X.c. Zhou, X.p. YanAbstract:The lubricated rolling Wear was simulated using a specially modified pin-on-disc tester. Wear particles were collected in running-in and steady state Wear stage, and analysed using a particle analyser, ferrography, confocal laser scanning microscopy and computer image Analysis techniques. Numerical parameters, Ra, Rq and Rsk were used to measure the evolutions of the surface alternations from the running-in to steady state Wear stage. Quantitative data on the evolutions of the surfaces of both the Wear Debris and the Wear components were presented in the paper. The knowledge obtained in this research is important for predicting Wear conditions using Wear Debris Analysis techniques, while further understanding of the Wear mechanisms and Wear characteristics has been also gained
Nadege Bouchonneau - One of the best experts on this subject based on the ideXlab platform.
-
a review of wind turbine bearing condition monitoring state of the art and challenges
Renewable & Sustainable Energy Reviews, 2016Co-Authors: Henrique Dias Machado De Azevedo, Alex Mauricio Araujo, Nadege BouchonneauAbstract:Since the early 1980s, wind power technology has experienced an immense growth with respect to both the turbine size and market share. As the demand for large-scale wind turbines and lor operation & maintenance cost continues to raise, the interest on condition monitoring system has increased rapidly. The main components of wind turbines are the focus of all CMS since they frequently cause high repair costs and equipment downtime. However, vast quantities of their failures are caused due to a bearing failure. Therefore, bearing condition monitoring becomes crucial. This paper aims at providing a state-of-the-art review on wind turbine bearing condition monitoring techniques such as acoustic measurement, electrical effects monitoring, power quality, temperature monitoring, Wear Debris Analysis and vibration Analysis. Furthermore, this paper will present a literature review and discuss several technical, financial and operational challenges from the purchase of the CMS to the wind farm monitoring stage.
