Local maximum synchroextracting wavelet transform: a tool for rotating machinery fault diagnosis under variable speed conditions

Abstract Time–frequency analysis (TFA) has proven to be a powerful technique for analyzing non-stationary signals. However, in practical applications such as mechanical equipment monitoring, the collected vibration signals often exhibit strong non-stationarity and are heavily contaminated by noise. These challenges significantly limit the accuracy and robustness of conventional TFA methods. To address this issue, this paper proposes a local maximum synchroextracting wavelet transform (LMSEWT), a novel enhancement of the synchroextracting transform framework based on the continuous wavelet transform. LMSEWT introduces an adaptive mechanism into the time-frequency (TF) reassignment process by leveraging the multi-scale characteristics of wavelets and a local maximum criterion to refine instantaneous frequency estimation. This approach not only improves the resolution and concentration of TF representations but also enhances noise robustness. The theoretical foundation and implementation strategy of LMSEWT are detailed in this study. Its effectiveness is further demonstrated through application to fault diagnosis of variable-speed machinery, where it is benchmarked against several traditional TFA methods. Experimental results confirm the superior performance and practical value of the proposed method.