Sensor Failure Detection, Identification, and Accommodation Using Fully Connected Cascade Neural Network
| Autor: | Maizura Mokhtar, Joe Howe, Saed Hussain |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Scheme (programming language)
Engineering Artificial neural network business.industry Yaw Control engineering Identification (information) Control and Systems Engineering Cascade Multilayer perceptron Control system Electrical and Electronic Engineering business Accommodation computer computer.programming_language |
| Zdroj: | IEEE Transactions on Industrial Electronics. 62:1683-1692 |
| ISSN: | 1557-9948 0278-0046 |
| DOI: | 10.1109/tie.2014.2361600 |
| Popis: | Modern control systems rely heavily on their sensors for reliable operation. Failure of a sensor could destabilize the system, which could have serious consequences to the system's operations. Therefore, there is a need to detect and accommodate such failures, particularly if the system in question is of a safety critical application. In this paper, a sensor failure detection, identification, and accommodation (SFDIA) scheme is presented. This scheme is based on the fully connected cascade (FCC) neural network (NN) architecture. The NN is trained using the neuron by neuron learning algorithm. This NN architecture is chosen because of its efficiency in terms of the number of neurons and the number of inputs required to solve a problem. The SFDIA scheme considers failures in pitch, roll, and yaw rate gyro sensors of an aircraft. A total of 105 experiments were conducted; out of which, only one went undetected. The SFDIA scheme presented here is efficient, compact, and computationally less expensive, in comparison to schemes using, for example, the popular multilayer perceptron NN. These benefits are inherited from the FCC NN architecture. |
| Databáze: | OpenAIRE |
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