Synchro-Reassigned Extracting Transform: An Effective Tool for Rotating Machinery Fault Diagnosis Under Varying Speed Condition

Time–frequency analysis (TFA) techniques offer valuable insights into the dynamic characteristics of nonstationary signals, making them suitable for diagnosing faults in rotating machinery operating under variable speed conditions. However, extracting meaningful features from time–frequency representations (TFRs) faces challenges due to energy spreading caused by complex modes and background noise. To address this issue, this article introduces a novel technique called the synchro-reassigned extracting transform (SRET). The SRET uses instantaneous frequency (IF) and group delay (GD) operators to extract and reassign energy coefficients simultaneously in both the frequency and time directions, enhancing the sharpness of TFRs. Theoretical analysis reveals the limitations of the synchroextracting transform (SET) when analyzing signals with both slowly and rapidly varying features, which the proposed SRET effectively overcomes. To optimize the computational efficiency, this article presents a discrete implementation algorithm for SRET. The effectiveness of SRET in analyzing time-varying signals and diagnosing bearing faults is demonstrated through simulations and two sets of bearing vibration data. In addition, the application of SRET in processing vibration signals from a wind turbine gearbox highlights its potential for fault diagnosis in rotating machinery.