Study on surface features of wear particles generated in wear process provides an insight into the progress of material failure of artificial joints. It is very important to quantify the surface features of wear particles in three dimensions. In this study, a new approach using atomic force microscopy was proposed to carry out 3D numerical surface characterization of wear debris generated from artificial joints. Atomic force microscopy combined with image processing techniques was used to acquire appropriate 3D images of wear debris. Computerized image analysis techniques were then used to quantify surface texture features of wear debris such as surface roughness parameters and surface texture index. The method developed from the present study was found to be feasible to quantity the surface characterization of nanoand micro-sized wear debris generated from artificial joints.
Gearboxes are extensively used in various areas including aircraft,mining,manufacturing,and agriculture,etc.Th...
Zhixiong Li 1,2,a,Xinping Yan 1,2,b,Chengqing Yuan 1,2,c and Li Li 3,d 1 Reliability Engineering Institute,School of Energy and Power Engineering,Wuhan University of Technology,Wuhan 430063,P.R.China
A marine propulsion system is a very complicated system composed of many mechanical components.As a result,the vibration signal of a gearbox in the system is strongly coupled with the vibration signatures of other components including a diesel engine and main shaft.It is therefore imperative to assess the coupling effect on diagnostic reliability in the process of gear fault diagnosis.For this reason,a fault detection and diagnosis method based on bispectrum analysis and artificial neural networks (ANNs) was proposed for the gearbox with consideration given to the impact of the other components in marine propulsion systems.To monitor the gear conditions,the bispectrum analysis was first employed to detect gear faults.The amplitude-frequency plots containing gear characteristic signals were then attained based on the bispectrum technique,which could be regarded as an index actualizing forepart gear faults diagnosis.Both the back propagation neural network (BPNN) and the radial-basis function neural network (RBFNN) were applied to identify the states of the gearbox.The numeric and experimental test results show the bispectral patterns of varying gear fault severities are different so that distinct fault features of the vibrant signal of a marine gearbox can be extracted effectively using the bispectrum,and the ANN classification method has achieved high detection accuracy.Hence,the proposed diagnostic techniques have the capability of diagnosing marine gear faults in the earlier phases,and thus have application importance.
