Gear Crack Detection Using Residual Signal and Empirical Mode Decomposition

Diagnosis of gearbox defects at an early stage is very important to avoid catastrophic failures. This article presents experimental results of tests made to evaluate the cracks of the cylindrical gears of a transfer case under advanced test conditions. For the diagnosis of a gearbox, various signal processing techniques are mainly used for the vibration study of the gears, such as: Fast Fourier Transform, synchronous time average, and time-based wavelet transformation, etc. Various methods can be found in the literature which can be used to calculate the residual signal (RS), however, in this paper, we suggest a new method combined empirical mode decomposition (EMD) technique with RS for detection of the crack gear. In order to extract the associated defect characteristics of the transfer box vibration signals, the EMD has been performed. The results show the effectiveness of the EMD method in the evaluation of tooth cracking in spur gears. This effectiveness can be proved by the obtained results of the experimental tests, which were presented and carried out on a test rig equipped with a transfer box.

Complementary ensemble adaptive sparsest narrow-band decomposition method and its applications to the gear crack fault diagnosis

Adaptive sparsest narrow-band decomposition is the most sparse solution to search for signals in the over-complete dictionary library containing intrinsic mode functions, which transform the signal decomposition into an optimization problem, but the calculation accuracy must be improved in the case of strong noise interference. Therefore, in combination with the algorithm of the complementary ensemble empirical mode decomposition, a new method of the complementary ensemble adaptive sparsest narrow-band decomposition is obtained. In the complementary ensemble adaptive sparsest narrow-band decomposition, the white noise opposite to the paired symbol is added to the target signal to reduce the reconstruction error and realize the adaptive decomposition of the signal in the process of optimizing the filter parameters. The analysis results of the simulation and experimental data show this method is superior to complementary ensemble empirical mode decomposition and adaptive sparsest narrow-band decomposition in inhibiting the mode confusion, endpoint effect, improving the component orthogonality and accuracy, and effectively identifying the gears fault types.