Integrated Prognostics Observer for Condition Monitoring of an Automated Manual Transmission Dry Clutch System

Sivakumar Ramalingam, Hanumath VV Prasad, and Srinivasa Prakash Regalla
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_17_028.pdf717.47 KBOctober 30, 2017 - 9:59pm

The closed loop feedback control system of an Automated Manual Transmission (AMT) electro-pneumatic clutch actuator is used for intelligent real time condition monitoring, enhanced diagnostics and prognostic health management of the dry clutch system, by integrating with the existing gearbox prognostics observer. The real time sensor data of the clutch actuator piston position is analysed for monitoring the condition of the clutch system. Original parameters of the new clutch are stored in the Electrically Erasable Programmable Read-only Memory (EEPROM) of the AMT controller and the real time data is used by the observer for assessing the degradation/wear of the frictional clutch parts. Also, clutch slip during torque transmission is monitored, using the engine speed and the gearbox input shaft speed from Controller Area Network (CAN). Condition monitoring of clutch system provides enhanced prognostic functionality for AMT system which ensures consistent clutch performance, gear shift quality and timely warning for re-calibration, repair and/or replacement of the critical wear and tear parts. Also, systematic analysis of the monitored data provides an accurate diagnosis of a developing fault. Thus, with the advanced control systems in place for AMT, a closed loop feedback based condition monitoring system is modeled for improved diagnostics and prognostics of AMT clutch system.

Publication Year: 
2017
Publication Volume: 
8
Publication Control Number: 
028
Page Count: 
9
Submission Keywords: 
Automated Manual Transmission
AMT
prognostics
diagnostics
Observer
condition monitoring
Dry Clutch
.
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Model-based methods for fault detection, diagnostics, and prognosis
Submitted by: 
  
 
 
 

follow us

PHM Society on Facebook Follow PHM Society on Twitter PHM Society on LinkedIn PHM Society RSS News Feed