Katunin, A., Amarowicz, M. and Chrzanowski, P. (2015) Faults Diagnosis Using Self-Organizing Maps: A Case Study on the DAMADICS Benchmark Problem. Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, ACSIS, 5, 1673-1681.
http://dx.doi.org/10.15439/2015F26

has been cited by the following article:

• TITLE: A Self-Learning Diagnosis Algorithm Based on Data Clustering

  AUTHORS: Dmitry Tretyakov

  KEYWORDS: Self-Learning, Diagnostics, Fault Detection, Clusters, K-Means, Turbomachine, Gas Turbine, Centrifugal Supercharger, Gas Compressor Unit

  JOURNAL NAME: Intelligent Control and Automation, Vol.7 No.3, August 10, 2016

  ABSTRACT: The article describes an approach to building a self-learning diagnostic algorithm. The self-learning algorithm creates models of the object under consideration. The models are formed periodically through a certain time period. The model includes a set of functions that can describe whole object, or a part of the object, or a specified functionality of the object. Thus, information about fault location can be obtained. During operation of the object the algorithm collects data received from sensors. Then the algorithm creates samples related to steady state operation. Clustering of those samples is used for the functions definition. Values of the functions in the centers of clusters are stored in the computer’s memory. To illustrate the considered approach, its application to the diagnosis of turbomachines is described.