In this paper we provide a framework for the design of a practical monitoring method with learning methods. We demonstrate
that three medical and industrial monitoring problems involve subproblems that can be tackled with our approach. Application
of interference removal, novelty detection and learning of a signature leads to a feasible monitoring method in these cases.
This work is supported by Technology Foundation STW, project DTN-44.3584. We thank TechnoFysica b.v. and Drs. Groeneveld,
Stam and Frietman for cooperation.
A. Ypma and O. Baunbæk-Jensen are with the Dutch Foundation for Neural Networks SNN Nijmegen and the TU Denmark, respectively.