Structural Analysis Based Sensor Placement for Diagnosis of Clutch Faults in Automatic Transmissions

2018-01-1357

04/03/2018

Features
Event
WCX World Congress Experience
Authors Abstract
Content
This paper describes a systematic approach to identify the best sensor combination by performing sensor placement analysis to detect and isolate clutch stuck-off faults in Automatic Transmissions (AT) based on structural analysis. When an engaged clutch in the AT loses pressure during operation, it is classified as a clutch stuck-off fault. AT can enter in neutral state because of these faults; causing loss of power at wheels. Identifying the sensors to detect and isolate these faults is important in the early stage of the AT development. A universal approach to develop a structural model of an AT is presented based on the kinematic relationships of the planetary gear set elements. Sensor placement analysis is then performed to determine the sensor locations to detect and isolate the clutch stuck-off faults using speed sensors and clutch pressure sensors. The proposed approach is then applied to a 10-Speed AT to demonstrate its effectiveness. A simulator is developed to qualitatively study the effects of clutch stuck-off faults on speeds of different elements in an AT. Simulator results are presented to support the sensor placement analysis. Later, a comparative analysis of different sensor sets based on the cost and performance is conducted to choose the optimal sensor combination. This paper concludes by discussing in detail the different sensor sets that give different fault isolation performance and suggests that only increasing number of sensors does not guarantee better fault isolation.
Meta TagsDetails
DOI
https://doi.org/10.4271/2018-01-1357
Pages
9
Citation
Deosthale, E., Ahmed, Q., Arasu, M., Rizzoni, G. et al., "Structural Analysis Based Sensor Placement for Diagnosis of Clutch Faults in Automatic Transmissions," SAE Technical Paper 2018-01-1357, 2018, https://doi.org/10.4271/2018-01-1357.
Additional Details
Publisher
Published
Apr 3, 2018
Product Code
2018-01-1357
Content Type
Technical Paper
Language
English