NDLI: A new unsupervised approach to fault detection and identification

Content Provider	IEEE Xplore Digital Library
Author	Sielly Jales Costa, B. Parvanov Angelov, P. Guedes, L.A.
Copyright Year	2014
Description	Author affiliation: Dept. of Comput. Eng., UFRN, Natal, Brazil (Guedes, L.A.) \|\| Sch. of Comput. & Commun., Lancaster Univ., Lancaster, UK (Parvanov Angelov, P.) \|\| IFRN, Natal, Brazil (Sielly Jales Costa, B.)
Abstract	In this paper, a new fully unsupervised approach to fault detection and identification is proposed. It is based on a two-stage algorithm and starts with the recursive density estimation (RDE) in the feature space. The choice of the features is important and in the real world process that we consider these are control and error related variables. The basis of the proposed approach is the fully unsupervised evolving classifier AutoClass which can be seen as an extension of the earlier one, but is using data clouds and data density information. It has to be stressed that the density in the data space is not the same as the well-known and widely used in statistics probability density function (pdf) although it looks similar. The density in the data space, D is pivotal and instrumental for anomaly detection. It can be calculated recursively, which makes it very efficient in terms of memory, computational power and, thus, applicable to on-line applications. Importantly, the proposed method not only can detect anomalies, but also can identify and diagnose the fault during the second stage of the process. While the first stage is centred around RDE, the second stage is based on the evolving fuzzy rule-based (FRB) classifier AutoClass. A key advantage of AutoClass is that it is fully unsupervised (there is no need to pre-specify the fuzzy rules, number of classes) and can start learning "from scratch". AutoClass can be initialised with some prior knowledge (assuming that it does exists) and evolve/develop it further, but that is not mandatory. This new approach is generic, but in this paper without limiting the concept it is validated on a lab based control kit. In this particular example, the features are the control and error signals. The results significantly outperform alternative methods, which is in addition to the advantages that the approach is autonomous.
Starting Page	1557
Ending Page	1564
File Size	2792769
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889973
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Fault detection Vectors Classification algorithms Process control Algorithm design and analysis Detection algorithms autonomous learning fault identification recursive density estimation evolving classifiers
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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