An approach is proposed for automatic fault detection in a population of mechatronic systems. The idea is to employ self-organizing algorithms that produce low-dimensional representations of sensor and actuator values on the vehicles, and compare these low-dimensional representations among the systems. If a representation in one vehicle is found to deviate from, or to be not so similar to, the representations for the majority of the vehicles, then the vehicle is labeled for diagnostics. The presented approach makes use of principal component coding and a measure of distance between linear sub-spaces. The method is successfully demonstrated using simulated data for a commercial vehiclepsilas engine coolant system, and using real data for computer hard drives. ; ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
A self-organized approach for unsupervised fault detection in multiple systems
2008-01-01
Conference paper
Electronic Resource
English
computer hard drives , coolants , engine coolant system , fault diagnosis , mechanical engineering computing , mechatronic systems , mechatronics , multiple systems , principal component coding , self-adjusting systems , self-organizing algorithms , unsupervised fault detection , vehicles , Computer Vision and Robotics (Autonomous Systems) , Datorseende och robotik (autonoma system)
DDC: | 629 |
Self-organized Modeling for Vehicle Fleet Based Fault Detection
British Library Conference Proceedings | 2008
|Self-organized Modeling for Vehicle Fleet Based Fault Detection
SAE Technical Papers | 2008
|A Self-Organized Multiple Model Approach for Neural-Adaptive Control of Jump Nonlinear Systems
British Library Conference Proceedings | 2000
|