A power information guided-variational mode decomposition (PIVMD) and its application to fault diagnosis of rolling bearing

As a powerful tool of mode component extraction, variational mode decomposition (VMD) has found its wide application in fault diagnosis. However, VMD still suffers from an unsolved problem. To be specific, how to adaptively determine the decomposition number, the initial center frequencies and the variable penalty factors in VMD according to the characteristics of vibration signal is still an unsolved problem. To address this problem, the authors propose a novel mode component extraction method, namely, the power information guided-variational mode decomposition (PIVMD). The essential idea of the proposed method is using the enveloped auto-regressive (AR) power spectrum of raw vibration signal to determine the VMD parameters (decomposition number, initial center frequencies and variable penalty factors). After determining these VMD parameters, an iterative search is conducted to obtain the optimal solution for constructed variational model. A noise reduction envelope spectrum is further used to enhance the weak fault features contained in the extracted mode component. A set of simulation signals representing the faulty characteristics of rolling bearing with inner/outer race faults is analyzed by the proposed PIVMD method to test its mode extraction ability. Then, an open dataset from Case Western Reserve University and a measured dataset from a steel mill in Ma Steel Co. as well as an experimental dataset collected from the authors' laboratory are analyzed to verify the fault diagnosis capability of the proposed PIVMD method. Meanwhile, the analyzed results are compared with those obtained from other mode decomposition methods to highlight the superiority of the proposed PIVMD method in terms of extracting mode components with rich fault information.