A novel vibration-based prognostic scheme for gear health management in surface wear progression of the intelligent manufacturing system

Gearbox has a compact structure, a stable transmission capability, and high transmission efficiency. Thus, it is widely applied and used as a critical transmission system in intelligent manufacturing systems, such as machine tools and robotics. The gearbox usually operates in harsh and non-stationary working environments, making the gear surface prone to wear. The progression of gear surface wear may lead to severe gear failures, such as gear tooth breakage and root crack, potentially damaging the whole gear transmission system. Therefore, it is essential to assess the gear surface wear progression and predict its remaining useful life (RUL) in order to ensure the reliable operation of the gear transmission system. To this end, this paper developed a novel gear wear prognostic scheme based on vibration analysis for gear health management. More specifically, a novel health indicator (HI) is first developed for gear wear monitoring in the proposed prognostic scheme. The novel HI, inferred from the cyclic correntropy and Wasserstein distance (WD), can accurately reflect the wear-induced cyclic correntropy spectra distribution change over time. Therefore, the novel HI can robustly evaluate the gear wear severity with high accuracy. With the developed HI, a network, namely the optimized gated recurrent unit (GRU), is applied for predicting the gear transmission system RUL during surface wear progression. As for the optimized GRU network, the genetic algorithm (GA) is applied to find the optimal hyperparameters adap-tively, which can significantly improve the practicality of the developed prognostic scheme. To conclude, the developed prognostic scheme can effectively reveal the gear wear propagation characteristics and predict the RUL accurately. A series of endurance tests are conducted to verify the effectiveness of the developed prognostic scheme for gear health management in surface wear progression.

Automated summary

Gearboxes are widely used in intelligent manufacturing systems and are prone to wear in harsh working environments. This paper presents a novel gear wear prognostic scheme using vibration analysis. It develops a new health indicator (HI) based on cyclic correntropy and Wasserstein distance to accurately evaluate gear wear severity. The optimized gated recurrent unit (GRU) network, with hyperparameters adaptively determined by genetic algorithm (GA), is applied to predict the remaining useful life (RUL) of the gear transmission system. The developed prognostic scheme effectively reveals gear wear characteristics and accurately predicts RUL.