Helical gear wear monitoring: Modelling and experimental validation

Abstract

Gear tooth surface wear is a common failure mode. It occurs over relatively long periods of service nonetheless, it degrades operating efficiency and lead to other major failures such as excessive tooth removal and catastrophic breakage. To develop accurate wear detection and diagnosis approaches at the early phase of the wear, this paper examines the gear dynamic responses from both experimental and numerical studies with increasing extents of wear on tooth contact surfaces. An experimental test facility comprising of a back-to-back two-stage helical gearbox arrangement was used in a run-tofailure test, in which variable sinusoidal and step increment loads along with variable speeds were applied and gear wear was allowed to progress naturally. A comprehensive dynamic model was also developed to study the influence of surface wear on gear dynamic response, with the inclusion of time-varying stiffness and tooth friction based on elasto-hydrodynamic lubrication (EHL) principles.The model consists of an 18 degree of freedom (DOF) vibration system, which includes the effects of the supporting bearings, driving motor and loading system. It also couples the transverse and torsional motions resulting from time-varying friction forces, time varying mesh stiffness and the excitation of different wear severities. Vibration signatures due to tooth wear severity and frictional excitations were acquired for the parameter determination and the validation of the model with the experimental results. The experimental test and numerical model results show clearly correlated behaviour, over different gear sizes and geometries. The spectral peaks at the meshing frequency components along with their sidebands were used to examine the response patterns due to wear. The paper concludes that the mesh vibration amplitudes of the second and third harmonics as well as the sideband components increase considerably with the extent of wear and hence these can be used as effective features for fault detection and diagnosis.

Fault Diagnosis of a Helical Gearbox Based on an Adaptive Empirical Wavelet Transform in Combination with a Spectral Subtraction Method

“…Many researchers have established dynamical simulation models to analyze gear faults [5]. Brethee et al [6] established an 18-degree-of-freedom vibration system to study the different degrees of tooth-surface wear. However, their analytical equations are very complex and inconvenient to use.…”

Nonlinear Dynamic Characteristics of Fixed‐Axis Gear Wear in Multistage Gear Transmission Systems

“…For wear fault diagnosis, most of the studies mainly use the signal processing method to extract fault signals, such as the gear wear monitoring by a modulation signal bispectrum based on motor current signal analysis [12], helical gear wear monitoring [13], and fault diagnosis of gear wearing based on order spectrum analysis [14]. Plint and Alliston-Greiner [15] proposed a new wear criterion: the energy pulse.…”

Damage Degree Detection of Cracks in a Locomotive Gear Transmission System

“…In 2015, Li and Peng developed a multi-degreeof-freedom nonlinear gear model that considers the meshing force and tribological force of the gear pair [9]. To analyze the influence of helical gear wear on the dynamic response of the gear transmission, Brethee et al developed a comprehensive dynamic model that consists of 18 degrees of freedom [10].…”