Paper Info
Title
A Self-Building and Cluster-Based Cognitive Fault Diagnosis System for Sensor Networks
Abstract
Cognitive fault diagnosis systems differentiate from more traditional solutions by providing online strategies to create and update the fault-free and the faulty classes directly from incoming data. This aspect is of paramount relevance within the big data framework, since measurements are there immediately processed to detect and identify the upsurge of potential faults. This paper introduces a novel cognitive fault diagnosis framework for processes described by nonlinear dynamic systems that inspects changes in the existing relationships among sensors. The proposed framework is based on an evolving clustering algorithm that operates in the parameter space of time invariant linear models approximating such relationships. During the operational life, parameter vectors associated with models thought not to belong to the nominal state are either labeled as outlier or fault. New classes of faults, here considered to propagate to the model parameters according to an abrupt profile, are created online as they appear. At the same time, existing classes can merge, depending on the information content carried by incoming data.
Year
DOI
Venue
2014
10.1109/TNNLS.2014.2303651
Neural Networks and Learning Systems, IEEE Transactions  
Keywords
DocType
Volume
Big Data,distributed sensors,fault diagnosis,pattern clustering,unsupervised learning,Big Data framework,adaptive learning,cluster-based cognitive fault diagnosis system,clustering algorithm,information content,invariant linear models,nonlinear dynamic systems,parameter space,parameter vectors,self-building-based cognitive fault diagnosis system,sensor networks,unsupervised clustering-labeling method,Adaptive learning,evolving clustering,fault diagnosis,fault diagnosis.
Journal
25
Issue
ISSN
Citations 
6
2162-237X
10
PageRank 
References 
Authors
0.56
24
3
Name
Order
Citations
PageRank
Cesare Alippi11040115.84
Manuel Roveri227230.19
Trovò, F.3100.56