An improved singular value decomposition-based method for gear tooth crack detection and severity assessment

Gear tooth crack fault detection and severity assessment using vibration analysis rely on the extraction of fault induced periodic impulses. Singular value decomposition (SVD)-based methods have been used by researchers for periodic impulses extraction. Most such methods extract high-energy signal components (SCs) but ignore weak-energy SCs. A newly reported reweighted SVD (RSVD) method uses periodic modulation intensity (PMI) criterion to measure how influential the periodic impulses are in each SC. This method effectively selects the correct SCs regardless of their energy. However, RSVD suffers from two issues: 1) it does not consider interference from non-fault related vibration components on the PMI, which leads to a high miss and/or false alarm rate; and 2) it directly conduct reweighted summation of all SCs with PMI values that exceed the threshold for signal reconstruction, which undermines periodic impulses extraction. To overcome these issues, this paper proposes an improved SVD-based method. First, the improved method adopts an auto-regression model-based baseline removal approach. SVD is applied to decompose the residual signal rather than the raw signal. Interference from non-fault related vibration components on the PMI is therefore eliminated. Second, the improved method selects the SCs such that the PMI of the reconstructed signal is maximized. Periodic impulses extraction is therefore strengthened. An experimental study was conducted. Results show that the improved method outperforms RSVD in terms of fault detection and severity assessment without creating a considerable computational burden. (C) 2019 Elsevier Ltd. All rights reserved.