Latest developments in gear defect diagnosis and prognosis: A review

Fault classification methods are a long-term research focus in both science society and engineering application. Generally, every real-world running mechanical system is exhibit personalized vibration behaviors and the corresponding fault samples of such systems are difficult to be obtained. Therefore, extreme learning machine (ELM), a typical fault classification method is failed to attain agreeable fault detection results. In this paper, a personalized fault diagnosis method using finite element method (FEM) simulation and ELM is proposed to detect faults in gears. The method includes three steps. Firstly, The FEM model of gears with faults is constructed to obtain fault samples (simulation signals). Secondly, to achieve ELM training process, the meshing frequency components of each simulation signal is separated into sub-signals and the corresponding time and time–frequency domains indicators are served as training samples. Finally, the measured vibration signals of gear transmission systems are employed as testing samples of trained ELM to recognize its fault types. The classification accuracy ratios of gear states in a cracked teeth of driving gear, a peeled teeth of driving gear, a broken teeth of driving gear, a peeled teeth of driving gear and a broken teeth of driven gear, a broken teeth of driving gear and a broken teeth of driven gear are 85%, 90%, 92.5%, 90% and 85% respectively. It is expect that the proposed personalized fault diagnosis method can set up a bridge between fault classification methods and real-world applications.

A personalized diagnosis method to detect faults in gears using numerical simulation and extreme learning machine

Fault mechanism and dynamic modeling of planetary gear with gear wear

Vibration-based monitoring and prediction of surface profile change and pitting density in a spur gear wear process

Gear fault models and dynamics-based modelling for gear fault detection – A review

Evaluating the influence of tooth surface wear on TVMS of planetary gear set

Investigation on the influence of spalling defects on the dynamic performance of planetary gear sets with sliding friction

The most common and effective way of rotating machinery diagnostics is to extract fault impact features from the vibration signals and to carry out further processing. As for planetary gear sets, because of the simultaneous mesh of multiple gears and the effect of the carrier rotation, the fault features are submerged in the strong harmonic signals and other noise. Due to the lack of prior information on the faults, the conventional fault diagnostic methods often fail to achieve satisfactory results. To address this problem, we give the prior information of a chipped planetary gear set by dynamics simulation, and utilize the information in the time domain to improve the diagnosis performance. Firstly, a pure torsional lumped parameter model is used to simulate the system vibration response with different degrees of failure. Through statistical analysis, the margin factor, the most sensitive indicator, is selected as prior information to reflect the local gear fault among several indexes. Finally, a weighted sparse representation method based on the prior information provided above is proposed to extract the impact features. Moreover, it is found that the extracted components have a strong–weak cyclic impact feature in the chipped planetary gear sets. The features and effectiveness of the method are verified by an experiment on a planetary gearbox test rig. To validate the superiority of the proposed method, comparisons are made among several state-of-the-art feature extraction methods.

https://iopscience.iop.org/article/10.1088/1361-6501/ab02d8/pdf

Wei Luo

Papers

Evaluating the influence of tooth surface wear on TVMS of planetary gear set

Fault mechanism and dynamic modeling of planetary gear with gear wear

Investigation on the influence of spalling defects on the dynamic performance of planetary gear sets with sliding friction

Weighted sparse representation based on failure dynamics simulation for planetary gearbox fault diagnosis

Dynamic modeling of planetary gear set with tooth surface wear