Incipient fault diagnosis for the cam-driven absolute gravimeter.

The vibration disturbance caused by incipient faults is an important factor affecting the measurement accuracy of the cam-driven absolute gravimeter. Based on the characteristics of the cam-driven absolute gravimeter, such as the small amplitude of the incipient faults, the inadequate representation of features for the faults, and hard-to-find in the noise, a novel method for incipient fault diagnosis of the cam-driven absolute gravimeter is put forward in this paper, which integrates the parameter-optimized Variational Mode Decomposition (VMD) with Light Gradient Boosting Machine (LightGBM). The sparrow search algorithm is used to optimize the VMD parameters. The parameter-optimized VMD algorithm is used to adaptively decompose the vibration signals of the gravimeter under different cases, and then an effective intrinsic mode function (IMF) is selected based on the Pearson correlation coefficient. Some high-frequency IMFs are subjected to adaptive noise reduction combined with low-frequency IMF reconstruction, and then the multi-scale permutation entropy with sensitive characteristics under different time scales is extracted as the fault feature vectors. The extracted multi-dimensional vector matrix is entered into the LightGBM classifier to realize the accurate diagnosis of the incipient faults for the cam-driven absolute gravimeter. The test results show that this method can effectively detect various incipient failures of the cam-driven absolute gravimeter, with an identification accuracy of 98.41%. With this method, the problem of low measurement accuracy for the cam-driven absolute gravimeter caused by the incipient faults is solved, and the rapid tracing and accurate positioning of these faults for the gravimeter are realized, promising a good prospect for engineering application.