This paper presents a model-free method for the online identification of sensor faults and learning of their fault dictionary. The method, designed having in mind Cyber-Physical Systems (CPSs), takes advantage of functional relationships among the datastreams acquired by CPS sensing units. Existing model-free change detection mechanisms are proposed to detect faults and identify the fault type thanks to a fault dictionary which is built over time. The main features of the proposed algorithm are its ability to operate without requiring any a priori information about the system under inspection or the nature of the possibly occurring faults. As such, the method follows the model-free approach, characterized by the fact the fault dictionary is constructed online once faults are detected. Whenever available, humans can be considered in the loop to label a fault or a fault class in the dictionary as well as introduce fault instances generated thanks to a priori information. Experimental results on both synthetic and real datasets corroborate the effectiveness of the proposed fault diagnosis system.

Online model-free sensor fault identification and dictionary learning in Cyber-Physical Systems / C. Alippi, S. Ntalampiras, M. Roveri (PROCEEDINGS OF ... INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS). - In: 2016 International Joint Conference on Neural Networks (IJCNN)[s.l] : IEEE, 2016. - ISBN 9781509006199. - pp. 756-762 (( convegno International Joint Conference on Neural Networks tenutosi a Vancouver nel 2016 [10.1109/IJCNN.2016.7727276].

Online model-free sensor fault identification and dictionary learning in Cyber-Physical Systems

S. Ntalampiras;M. Roveri
2016

Abstract

This paper presents a model-free method for the online identification of sensor faults and learning of their fault dictionary. The method, designed having in mind Cyber-Physical Systems (CPSs), takes advantage of functional relationships among the datastreams acquired by CPS sensing units. Existing model-free change detection mechanisms are proposed to detect faults and identify the fault type thanks to a fault dictionary which is built over time. The main features of the proposed algorithm are its ability to operate without requiring any a priori information about the system under inspection or the nature of the possibly occurring faults. As such, the method follows the model-free approach, characterized by the fact the fault dictionary is constructed online once faults are detected. Whenever available, humans can be considered in the loop to label a fault or a fault class in the dictionary as well as introduce fault instances generated thanks to a priori information. Experimental results on both synthetic and real datasets corroborate the effectiveness of the proposed fault diagnosis system.
Sensor fault identification; fault dictionary learning; linear time invariant models; Hidden Markov models
Settore INF/01 - Informatica
2016
IEEE Computational Intelligence Society (IEEE CIS)
Book Part (author)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/615050
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