Self-organising symbolic aggregate approximation for real-time fault detection and diagnosis in transient dynamic systems

Gallimore, Michael and Bingham, Chris and Riley, Mike (2017) Self-organising symbolic aggregate approximation for real-time fault detection and diagnosis in transient dynamic systems. In: 15th IEEE International Symposium on Applied Machine Intelligence and Informatics, 26-28 January 2017, Slovakia.

Documents
SAMI_2017_paper_46.pdf
[img]
[Download]
[img]
Preview
PDF
SAMI_2017_paper_46.pdf - Whole Document

470kB
Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive

Abstract

The development of accurate fault detection and diagnosis (FDD) techniques are an important aspect of monitoring system health, whether it be an industrial machine or human system. In FDD systems where real-time or mobile monitoring is required there is a need to minimise computational overhead whilst maintaining detection and diagnosis accuracy. Symbolic Aggregate Approximation (SAX) is one such method, whereby reduced representations of signals are used to create symbolic representations for similarity search. Data reduction is achieved through application of the Piecewise Aggregate Approximation (PAA) algorithm. However, this can often lead to the loss of key information characteristics resulting in misclassification of signal types and a high risk of false alarms. This paper proposes a novel methodology based on SAX for generating more accurate symbolic representations, called Self-Organising Symbolic Aggregate Approximation (SOSAX). Data reduction is achieved through the application of an optimised PAA algorithm, Self-Organising Piecewise Aggregate Approximation (SOPAA). The approach is validated through the classification of electrocardiogram (ECG) signals where it is shown to outperform standard SAX in terms of inter-class separation and intra-class distance of signal types.

Keywords:Similarity search, data reduction, fault detection, Symbolic Aggregate Approximation
Subjects:H Engineering > H100 General Engineering
Divisions:College of Science > School of Engineering
ID Code:26026
Deposited On:03 Feb 2017 14:09

Repository Staff Only: item control page