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

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. (C) 2020 Elsevier B.V. All rights reserved.