Fast nonlinear blind deconvolution for rotating machinery fault diagnosis

Abstract

Sparse optimization based early fault diagnosis method is drawing more and more attention. In these methods, the objective function is usually a sparsity measure which can represent the impulsive signature produced by the mechanical impacts between rotating components. Therefore, the key point is the fault representation and convergence stability of the objective function. Inspired by this, fast nonlinear blind deconvolution algorithm is proposed for early fault diagnosis of rotating machinery. First, sigmoid function is developed to the generalized form to improve the fault representation ability of the objective function under noisy environment. The nonlinear mapping and sparse expression ability are discussed in detail. Then, Gaussian fitting window function and L1/2 penalty of filter are used to improve the distribution and performance of the weight vector. Finally, simulation data and early bearing fault data are employed to verify the effectiveness and determine optimal parameter setting of the proposed method. Based on the comparison results with the existing methods, the proposed method is found to be a promising method for the early-stage fault diagnosis, which can significantly improve the noise adaptability, computation effectiveness and robustness of fault diagnosis.