Relative vibration identification of cutter and workpiece based on improved bidimensional empirical mode decomposition

In the process of cutting,the relative vibration between the cutter and the workpiece has an important effect on the surface topography.In this study,the bidimensional empirical mode decomposition(BEMD)method is used to identify such effect.According to Riesz transform theory,a type of isotropic monogenic signal is proposed.The boundary data is extended on the basis of a similarity principle that deals with serious boundary effect problem.The decomposition examples show that the improved BEMD can effectively solve the problem of boundary effect and decompose the original machined surface topography at multiple scales.The characteristic surface topography representing the relative vibration between the cutter and the workpiece through feature identification is selected.In addition,the spatial spectrum analysis of the extracted profile is carried out.The decimal part of the frequency ratio that has an important effect on the shape of the contour can be accurately identified through contour extraction and spatial spectrum analysis.The decomposition results of simulation and experimental surface morphology demonstrate the validity of the improved BEMD algorithm in realizing the relative vibration identification between the cutter and the workpiece.

Study on the Algorithm of Vibration Source Identification Based on the Optical Fiber Vibration Pre-Warning System

One of the key technologies for optical fiber vibration pre-warning system （OFVWS） refers to identifying the vibration source accurately from the detected vibration signals. Because of many kinds of vibration sources and complex geological structures, the implement of identifying vibration sources presents some interesting challenges which need to be overcome in order to achieve acceptable performance. This paper mainly conducts on the time domain and frequency domain analysis of the vibration signals detected by the OFVWS and establishes attribute feature models including an energy information entropy model to identify raindrop vibration source and a fundamental frequency model to distinguish the construction machine and train or car passing by. Test results show that the design and selection of the feature model are reasonable, and the rate of identification is good.

Nonlinear identification of electro-magnetic force model

Conventional attractive magnetic force models (proportional to the coil current squared and inversely proportional to the gap squared) cannot simulate the nonlinear responses of magnetic bearings in the presence of electromagnetic losses,flux leakage or saturation of iron.In this paper,based on results from an experimental set-up designed to study magnetic force,a novel parametric model is presented in the form of a nonlinear polynomial with unknown coefficients.The parameters of the proposed model are identified using the weighted residual method.Validations of the model identified were performed by comparing the results in time and frequency domains.The results show a good correlation between experiments and numerical simulations.