Papers by Keyword: Autoregressive Models

Abstract: This paper presents a combined approach for sensor fault identification looking for changes within one channel on one hand and for changes between the different channels on the other hand. The first method is based on the identification of autoregressive (AR) models from the reference time signals for each sensor channel separately. The reference models are then used for the prediction of the future sensors signals. The statistical properties of the residuals between this prediction and the true measurement allow a statement about changes of the sensor signals. The second method is based on the concept of mutual information between two signals X and Y from two different sensors. Mutual information or transinformation measures the information about the channel X that is shared by Y. This requires a certain redundancy of information represented in the different sensor signals. It can be seen that the mutual information changes as soon as a sensor fault occurs because the sensor fault information is not present in the other sensor signals.

Abstract: Time series based Structural Health Monitoring (SHM) methods are being increasingly explored. In this study, Autoregressive (AR) models were used to fit the acceleration time histories of a 3-storey laboratory structure under excitation by earthquake records in several damaged and undamaged states. The coefficients of the AR models were used as inputs into an Artificial Neural Network (ANN) and the ANN was trained to relate the AR coefficients to the damage at each storey. The results showed that proposed method was able to detect, locate and quantify the damage in the structure with a very high accuracy.

Abstract: The mostly working time of airborne electronic equipment is under preliminary depletion failure phase, and inspection & maintenance at intervals can’t lower the failure probability. In this paper, the law of airborne electronic equipment failure is introduced firstly. Then, methods for failure prediction are summarized and analyzed. Finally, an example for predicting the airborne radar failure using the Auto-Regressive (AR) and Support Vector Regression (SVR) model is presented. On this basis, it is possible to achieve the goal that increases the reliability in working phase and establish a more scientific maintenance system and to assure the safety of airborne electronic equipment.