Involute gear

Process (computing)

Engineering

Tooth surface

Materials science

Operating system

Vibration

Computer science

Acoustics

Mechanical engineering

Involute

Physics

Spur

Image (mathematics)

Gear tooth

Automotive engineering

Artificial intelligence

Composite material

Structural engineering

Forensic engineering

Noise (video)

Spur gear

Wear

Gear wear introduces geometric deviations in gear teeth and alters the load distribution across the tooth surface. Wear also increases the gear transmission error, generally resulting in increased vibration, noise and dynamic loads. This altered loading in turn promotes wear, forming a feedback loop between gear surface wear and vibration. Having the capability to monitor and predict the gear wear process would bring enormous benefits in cost and safety to a wide range of industries, but there are currently no reliable, effective and efficient tools to do so. This paper develops such tools using vibration-based methods. For this purpose, a vibration-based scheme for updating a wear prediction model is proposed. In the proposed scheme, a dynamic model of a spur gear system is firstly developed to generate realistic vibrations, which allows a quantitative study of the effects of gear tooth surface wear on gearbox vibration responses. The sliding velocity and contact forces from the model are used in combination with the well-known Archard wear model to calculate the wear depth at each contact point in mesh. The worn gear tooth profile is then fed back into the dynamic model as a new geometric transmission error, which represents the deviation of the profile from an ideal involute curve and is thus zero for perfect gears. New vibration responses and tooth contact forces are then obtained from the model, and the process repeated to generate realistic gear wear profiles of varying severities. Since the wear coefficient in the model is not constant during the wear process (and in any case is difficult to estimate initially), measured vibrations are compared with those generated by the model, so as to update the coefficient when a deviation from predictions is detected. With the continually updated dynamic wear model, the wear process can be well monitored, and at any particular time the best possible prediction of remaining useful life can be achieved. The paper illustrates the ability and effectiveness of the proposed scheme using measurements from a laboratory gear rig.

Vibration-based updating of wear prediction for spur gears