Time–frequency representation of adjacent multi-component signals driven by IVKF-MPRTF and its application for mechanical equipment fault diagnosis

Rotating machinery is characterized by time-varying operating conditions and complex structures. The balance of its internal structure gradually changes with the variation of external working conditions. The vibration signals of rolling bearings that are key components exhibit obvious nonstationary characteristics at low speeds and heavy loads. Furthermore, mixed faults and modulation phenomena will generate time-varying multicomponent signals with adjacent fault characteristic frequencies. The existing time–frequency representation (TFR) algorithms for processing nonstationary signals with adjacent frequencies have limitations such as low time–frequency resolution and excessive parameter dependence. Therefore, a novel time–frequency analysis (TFA) framework for adjacent multicomponent signals driven by improved Vold–Kalman filtering-modified parameterized resampling time–frequency (IVKF-MPRTF) transform is presented in this study. First, this article introduces multicomponent signal time–frequency curve extraction to obtain the initial frequency information of the measured vibration signal, which addresses the issue of Vold–Kalman filtration (VKF) relying on a priori frequency information, and thus forms the improved Vold–Kalman filtering (IVKF). Then, the vibration signal can be precisely decomposed into adjacent components by the presented IVKF. Second, this article introduces local maximum frequency allocation operator and synchroextracting operator to compensate for the blur time–frequency resolution by parameterized resampling time–frequency transform, thus forming a modified parameterized resampling time–frequency (MPRTF) transform. By MPRTF, high-resolution TFR of multiple sets of single components can be obtained, and thus an enhanced TFR of nonstationary multicomponent signals is achieved through time–frequency fusion. Finally, the superiority of the adjacent multicomponent signals identification by the proposed IVKF-MPRTF is verified through the experimental signal analysis.