TY  - CONF
ID  - jaeger_assessing_2014
T1  - Assessing neural networks for sensor fault detection
A1  - Jäger, Georg
A1  - Zug, Sebastian
A1  - Brade, Tino
A1  - Dietrich, André
A1  - Steup, Christoph
A1  - Moewes, Christian
A1  - Cretu, Ana-Maria
TI  - Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA)
Y1  - 2014
SP  - 70
EP  - 75
PB  - IEEE Press
CY  - Ottawa, ON
AD  - Piscataway, NJ, USA
SN  - 978-1-4799-2613-8
UR  - http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6841441
M2  - doi: 10.1109/CIVEMSA.2014.6841441
KW  - Artificial Intelligence
KW  - fault diagnosis
KW  - Intelligent Sensors
KW  - Neural Networks
N2  - The idea of “smart sensing” includes a permanent monitoring and evaluation of sensor data related to possible measurement faults. This concept requires a fault detection chain covering all relevant fault types of a specific sensor. Additionally, the fault detection components have to provide a high precision in order to generate a reliable quality indicator. Due to the large spectrum of sensor faults and their specific characteristics these goals are difficult to meet and error prone. The developer manually determines the specific sensor characteristics, indicates a set of detection methods, adjusts parameters and evaluates the composition. In this paper we exploit neural-network approaches in order to provide a general solution covering typical sensor faults and to replace complex sets of individual detection methods. For this purpose, we identify an appropriate set of fault relevant features in a first step. Secondly, we determine a generic neural-network structure and learning strategy adaptable for detecting multiple fault types. Afterwards the approach is applied on a common used sensor system and evaluated with deterministic fault injections.
ER  -