A self-organized approach for unsupervised fault detection in multiple systems

An approach is proposed for automatic fault detection in a population of mechatronic systems. The idea is to employ self-organizing algorithms that produce lowdimensional 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 subspaces. The method is successfully demonstrated using simulated data for a commercial vehicle’s engine coolant system, and using real data for computer hard drives.

• Type: Conference
• Year: 2008
• Where: ICPR
• Authors: Thorsteinn S. Rögnvaldsson, Georg Panholzer, Stefan Byttner, Magnus Svensson
• Keywords: Automatic Fault Detection, Computer Vision, ICPR 2008, Low-dimensional Representations, Lowdimensional Representations