Projects per year
Abstract
The emergence of large sensor networks has facilitated the collection of large amounts of real-time data to monitor and control complex engineering systems. However, in many cases the collected data may be incomplete or inconsistent, while the underlying environment may be time-varying or unformulated. In this paper, we develop an innovative cognitive fault diagnosis framework that tackles the above challenges. This framework investigates fault diagnosis in the model space instead of the signal space. Learning in the model space is implemented by fitting a series of models using a series of signal segments selected with a sliding window. By investigating the learning techniques in the fitted model space, faulty models can be discriminated from healthy models using a one-class learning algorithm. The framework enables us to construct a fault library when unknown faults occur, which can be regarded as cognitive fault isolation. This paper also theoretically investigates how to measure the pairwise distance between two models in the model space and incorporates the model distance into the learning algorithm in the model space. The results on three benchmark applications and one simulated model for the Barcelona water distribution network confirm the effectiveness of the proposed framework.
Original language | English |
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Article number | 6515601 |
Pages (from-to) | 124-136 |
Number of pages | 13 |
Journal | IEEE Transactions on Neural Networks and Learning Systems |
Volume | 25 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Jan 2014 |
Keywords
- Cognitive fault diagnosis
- fault detection
- learning in the model space
- one class learning
- reservoir computing (RC)
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Computer Science Applications
- Software
- Medicine(all)
Fingerprint
Dive into the research topics of 'Learning in the model space for cognitive fault diagnosis'. Together they form a unique fingerprint.Projects
- 2 Finished
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Personalised Medicine through Learning in the Model Space
Engineering & Physical Science Research Council
1/10/13 → 31/03/17
Project: Research Councils
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Unified probabilistic modelleing of adaptive spatial temporal structures in the human brain
Tino, P. & Kourtzi, Z.
Biotechnology & Biological Sciences Research Council
1/10/10 → 30/03/14
Project: Research Councils