Time–frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform

The vibration data, especially those collected during the system run-up and run-down periods, contain rich information for gearbox condition monitoring. Time–frequency (TF) signal analysis is an effective tool to detect gearbox faults under varying shaft speed. However, the feature of the amplitude modulated–frequency modulated (AM–FM) gearbox fault signal usually cannot be directly extracted from the blurred time–frequency representation (TFR) caused by the time-varying frequency and noisy multicomponent measurement. As such, we propose to use a generalized synchrosqueezing transform (GST)-based TF method to detect and diagnose gearbox faults. With this method, the original vibration signal is first mapped into another analytical signal to facilitate synchrosqueezing of the TF picture. A time-scale domain restoration process is then applied to recover the instantaneous frequency profile with concentrated TFR. The gearbox fault, if any, can then be detected by observing the presence of the meshing frequency and sideband components in the TFR. The faulty gear can be identified via frequency relation analysis of AM–FM components. The proposed method is evaluated using both simulated and experimental gearbox vibration signals. The results show that the proposed approach is effective for gearbox condition monitoring.

► A generalized synchrosqueezing transform (GST)-based method is applied to gearbox monitoring.
► This GST-based method can alleviate the time-dimension spread problem.
► It can better reveal gearbox faults without excessive computational requirements.