A new probabilistic neural network for fault detection in MEMS

Authors:
Reza Asgary;Karim Mohammadi
Affiliations:
IranUniversity of Science and Technology, Department of Electrical engineering;IranUniversity of Science and Technology, Department of Electrical engineering
Venue:
ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
Year:
2005

Citing 3
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Abstract

Micro Electro Mechanical Systems will soon usher in a new technological renaissance. Learn about the state of the art, from inertial sensors to microfluidic devices [1]. Over the last few years, considerable effort has gone into the study of the failure mechanisms and reliability of MEMS. Although still very incomplete, our knowledge of the reliability issues relevant to MEMS is growing. One of the major problems in MEMS production is fault detection. After fault diagnosis, hardware or software methods can be used to overcome it. Most of MEMS have nonlinear and complex models. So it is difficult or impossible to detect the faults by traditional methods, which are model-based.In this paper, we use Robust Heteroscedastic Probabilistic Neural Network, which is a high capability neural network for fault detection. Least Mean Square algorithm is used to readjust some weights in order to increase fault detection capability.