Zhijian Wang

TL;DR: The article preprocesses the composite fault with ensemble empirical mode decomposition (EEMD) and then reconstructs the intrinsic mode function with the same time scale and proposes kurtosis spectral entropy as the objective function and uses the proposed method to search the complex fault pulse signals in strong noise environment.

TL;DR: A multi-objective particle swarm optimization (MOPSO) algorithm is proposed to optimize the parameters of VMD, and it is applied to the composite fault diagnosis of the gearbox.

TL;DR: An adaptive maximum cyclostationarity blind deconvolution (ACYCBD) is proposed, aiming at the determination of cyclic frequency set estimation method based on autocorrelation function of morphological envelope and the validity of the method is verified.

TL;DR: An adaptive spectrum mode extraction method for variational mode decomposition is proposed, which shows that the proposed method is more advantageous for the fault feature extraction of rolling bearings.

TL;DR: The newly proposed neural network named capsules network takes into account the size and location of the image and is applied in intelligent fault diagnosis, so as to improve the classification accuracy of Intelligent fault diagnosis.