Proceedings of the 4th Conference on European Computing Conference, 2010
... 3. George Edward Pelham Box , Gwilym Jenkins, Time Series Analysis, Forecasting and Control, ... more ... 3. George Edward Pelham Box , Gwilym Jenkins, Time Series Analysis, Forecasting and Control, Holden-Day, Incorporated, 1990. ... 9. Danilo P. Mandic , Jonathon Chambers, Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability, John Wiley & ...
Prognostic aims at estimating the remaining useful life (RU L) of a degrading equipment, i.e at p... more Prognostic aims at estimating the remaining useful life (RU L) of a degrading equipment, i.e at predicting the life time at which a component or a system will be unable to perform a desired function. This task is achieved through essential steps of data acquisition, feature extraction and selection, and prognostic modeling. This paper emphasizes on the selection phase and aims at showing that it should be performed according to the predictability of features: as there is no interest in retaining features that are hard to be predicted. Thereby, predictability is defined and a feature selection procedure based on this concept is proposed. The effectiveness of the approach is judged by applying it on a real-world case: through comparison is made in order to show that the better predictable features lead to better RU L estimation.
Within condition based maintenance (CBM), the whole as-pect of prognostics is composed of various... more Within condition based maintenance (CBM), the whole as-pect of prognostics is composed of various tasks from multi-dimensional data to remaining useful life (RUL) of the equip-ment. Apart from data acquisition phase, data-driven prog-nostics is achieved in three main steps: features extraction and selection, features prediction, and health-state classifica-tion. The main aim of this paper is to propose a way of im-proving existing data-driven procedure by assessing the pre-dictability of features when selecting them. The underlying idea is that prognostics should take into account the ability of a practitioner (or its models) to perform long term predic-tions. A predictability measure is thereby defined and applied to temporal predictions during the learning phase, in order to reduce the set of selected features. The proposed methodol-ogy is tested on a real data set of bearings to analyze the effec-tiveness of the scheme. For illustration purpose, an adaptive neuro-fuzzy inference ...
This paper presents an application of an ANN (Artificial Neural Network) of a RNRF type (Recurren... more This paper presents an application of an ANN (Artificial Neural Network) of a RNRF type (Recurrent Network with Radial basis Function) in controlling a linear system. The performance of ANN-based control solution is compared with a classic controller and the results show that ANN behaves better than the classic controller. MATLAB simulation performed show that the coupling between the ANN and a proportional controller gives the best performance.
Proceedings of the 4th Conference on European Computing Conference, 2010
... 3. George Edward Pelham Box , Gwilym Jenkins, Time Series Analysis, Forecasting and Control, ... more ... 3. George Edward Pelham Box , Gwilym Jenkins, Time Series Analysis, Forecasting and Control, Holden-Day, Incorporated, 1990. ... 9. Danilo P. Mandic , Jonathon Chambers, Recurrent Neural Networks for Prediction: Learning Algorithms,Architectures and Stability, John Wiley & ...
Prognostic aims at estimating the remaining useful life (RU L) of a degrading equipment, i.e at p... more Prognostic aims at estimating the remaining useful life (RU L) of a degrading equipment, i.e at predicting the life time at which a component or a system will be unable to perform a desired function. This task is achieved through essential steps of data acquisition, feature extraction and selection, and prognostic modeling. This paper emphasizes on the selection phase and aims at showing that it should be performed according to the predictability of features: as there is no interest in retaining features that are hard to be predicted. Thereby, predictability is defined and a feature selection procedure based on this concept is proposed. The effectiveness of the approach is judged by applying it on a real-world case: through comparison is made in order to show that the better predictable features lead to better RU L estimation.
Within condition based maintenance (CBM), the whole as-pect of prognostics is composed of various... more Within condition based maintenance (CBM), the whole as-pect of prognostics is composed of various tasks from multi-dimensional data to remaining useful life (RUL) of the equip-ment. Apart from data acquisition phase, data-driven prog-nostics is achieved in three main steps: features extraction and selection, features prediction, and health-state classifica-tion. The main aim of this paper is to propose a way of im-proving existing data-driven procedure by assessing the pre-dictability of features when selecting them. The underlying idea is that prognostics should take into account the ability of a practitioner (or its models) to perform long term predic-tions. A predictability measure is thereby defined and applied to temporal predictions during the learning phase, in order to reduce the set of selected features. The proposed methodol-ogy is tested on a real data set of bearings to analyze the effec-tiveness of the scheme. For illustration purpose, an adaptive neuro-fuzzy inference ...
This paper presents an application of an ANN (Artificial Neural Network) of a RNRF type (Recurren... more This paper presents an application of an ANN (Artificial Neural Network) of a RNRF type (Recurrent Network with Radial basis Function) in controlling a linear system. The performance of ANN-based control solution is compared with a classic controller and the results show that ANN behaves better than the classic controller. MATLAB simulation performed show that the coupling between the ANN and a proportional controller gives the best performance.
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