Application of Auto-Associative Neural Networks for Sensor Diagnostics

In this book, we address the problem of sensor fault diagnosis in complex systems. The motivation for this work is the common problem encountered in industrial setting, i.e. sensor shift, drift and outright failure. The approach proposed in this paper is based on Auto-Associative Neural Networks but has been extended to address some intrinsic deficiencies of these types of networks in practical setting. In particular, it is shown that the proposed approach provides the basic functionality needed for sensor fault detection in a multi-sensor environment with limited additional computational burden.

Autorentext

Massieh Najafi Received his PhD in Mechanical Engineering from the University of California at Berkeley and his master s degree from Texas A&M University. His is currently a research fellow at Lawrence Berkeley National Laboratory focusing on sensor network, fault detection and diagnosis, controls, energy efficiency, and building HVAC systems.