Jinshan Lin

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Empirical mode decomposition (EMD) is a powerful tool for the non-stationary and nonlinear signal analysis and has attracted considerable attention recently. However, one of primary problems existing in the EMD is the mode mixing, which makes the physical meaning of decomposition results obscure. The ensemble EMD (EEMD) is presented to alleviate the(More)
Ensemble empirical mode decomposition (EEMD) is a noiseassisted method and also a significant improvement on empirical mode decomposition (EMD). However, the EEMD method lacks a guide to choosing the appropriate amplitude of added noise and its computation efficiency is fairly low. To alleviate the problems of the EEMD method, the improved complementary(More)
This study examined scaling properties of an increment series from rotating machinery. Moreover, two fluctuation parameters for the smallest and largest time scales of a scaling range served as a pair of fluctuation parameters to describe system conditions. Therefore, an interesting phenomenon is observed: the data points, each representing a pair of(More)
Pure W and W-(2%, 5%, 10%) Lu alloys were manufactured via mechanical alloying for 20 h and a spark plasma sintering process at 1,873 K for 2 min. The effects of Lu doping on the microstructure and performance of W were investigated using various techniques. For irradiation performance analysis, thermal desorption spectroscopy (TDS) measurements were(More)
Conventional techniques are unfit for processing the non-stationary and nonlinear signal, whereas the empirical mode decomposition (EMD) is a powerful tool for the non-stationary and nonlinear signal analysis and has aroused wide concern recently. However, one principal shortcoming occurring in the EMD is the mode mixing, which makes the physical meaning of(More)
The paper introduces the method for designing the gearbox fault diagnosis system based on LabVIEW. The hardware system consists of acceleration transducer, signal conditioning card, data acquisition card, and PC. The software system with friendly interface is designed based on LabVIEW and the combination of wavelet transform and ensemble empirical mode(More)
Noise is always resident in vibration data collected from rotating machinery and different noise levels may relate to different mechanical conditions. As a result, the noise level can seemingly be exploited to evaluate running conditions of rotating machinery. To this end, this paper examined scaling properties of a white-noise series and its increment(More)
  • Jinshan Lin
  • The 2nd International Conference on Information…
  • 2010
The paper utilizes ensemble empirical mode decomposition (EEMD) and Hilbert marginal spectrum for the fault diagnosis of the reciprocating compressor on the offshore platform of WZ12-1, aiming at the non-stationary and nonlinear characteristics of vibration signals collected from the faulty compressor. First, the EEMD algorithm self-adaptively anti-aliasing(More)
  • Jinshan Lin
  • 2010 3rd International Congress on Image and…
  • 2010
The paper utilizes the neural network method to identify the loads of the WZ12–1 offshore platform. Firstly, the finite element model of the WZ12–1 platform is build using the software of ANSYS. By applying loads to the identification points of the finite element model, we can obtain the data for training a neural network model. Then, a(More)