Fault diagnosis of rolling bearing under time-varying rotational speeds via improved S high-order synchroextracting...

In rotating machinery condition monitoring, there is a growing demand for diagnosing bearing faults under time-varying rotational speeds using the tacholess order tracking (TLOT) technique. A critical step in TLOT is the rapid and accurate estimation of rotational frequency via time-frequency analysis (TFA). However, the current TFA methods often suffer from issues such as weak time-frequency energy concentration, uncertainty in window width selection, or high computational complexity. Moreover, achieving a high-resolution time-frequency representation (TFR) remains challenging, particularly for strong frequency modulation and amplitude modulation (FM-AM) signals. To overcome these limitations, this article introduces an improved S high-order synchroextracting transform (ISHSET). First, a novel single-parameter adaptive window function is designed to minimize the computational complexity while enhancing time-frequency energy concentration. Subsequently, an iterative estimator capable of estimating high-order instantaneous frequency (IF) is developed to accurately approximate the actual frequency values. Finally, the synchroextracting framework is incorporated to further elevate the resolution and readability of the TFR. Simulation analysis results indicate that ISHSET achieves a higher resolution, a more accurate and clearer characterization of the time-varying properties, and better noise robustness. Experimental validation through rolling bearing fault cases not only demonstrates the efficacy of the proposed method in estimating rotational frequency but also substantiates its superiority and practicality in extracting fault characteristics from rolling bearings under time-varying rotational speeds.