Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Novelty detection: a review—part 1: statistical approaches
Signal Processing
ACM Computing Surveys (CSUR)
Semi-supervised classification method for dynamic applications
Fuzzy Sets and Systems
Auto-adaptive and dynamical clustering neural network
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
DDD: A New Ensemble Approach for Dealing with Concept Drift
IEEE Transactions on Knowledge and Data Engineering
| Hi-index | 0.00 |
In this paper, we present a methodology for drift detection and characterization. Our methodology is based on extracting indicators that reflect the health state of a system. It is situated in an architecture of fault diagnosis/prognosis of dynamical system that we present in this paper. A dynamical clustering algorithm is used as a major tool. The feature vectors are clustered and then the parameters of these clusters are updated as each feature vector arrives. The cluster parameters serve to compute indicators for drift detection and characterization. Then, a prognosis block uses these drift indicators to estimate the remaining useful life. The architecture is tested on a case study of a tank system with different scenarios of single and multiple faults, and with different dynamics of drift.