Prediction for condition monitoring of wear and film thickness in a gear box

Title:

Prediction for condition monitoring of wear and film thickness in a gear box

Creator:

Krishnakumari, A., Elaya Perumal, A., and Panda, R. C.

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:0a306328-ca33-47ee-bb09-680cc4dbf1d9
uuid:0a306328-ca33-47ee-bb09-680cc4dbf1d9

Subject:

lubrication film thickness, gear failure, artificial neural network, elastohydrodynamic, and condition monitoring

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Gears are used to transmit power and motion in mechanical, electrical and chemical process industries. Influenced by vibration, torque, temperature, lubrication & specific film thickness, the gear teeth contacts may experience change leading to unexpected failures such as wear, scutting, pitting and micro-pitting on teeth surface. In order to avoid these damages, continuous monitoring is essential using knowledge based systems, Generic capability of artificial neural network (ANN) is exploited to formulate prediction and classification based on heuristic models of condition of lubricating oil in spur gears. Based on the loading conditions such as vibration, temperature and torque, the algorithm predicts film thickness to classify oil conditions as elastohydrodynamic (EHD), mixed wear and severe wear that helps in detecting faults in gear operation. and Obsahuje seznam literatury

Language:

English

Rights:

http://creativecommons.org/licenses/by-nc-sa/4.0/
policy:public

Coverage:

393-405

Source:

Engineering mechanics: international journal for theoretical and applied mechanics | 2012 Volume:19 | Number:6

Harvested from:

CDK

Metadata only:

false