Chronicle Discovery for Diagnosis from Raw Data: A Clustering Approach - LAAS - Laboratoire d'Analyse et d'Architecture des Systèmes Access content directly
Conference Papers Year : 2018

Chronicle Discovery for Diagnosis from Raw Data: A Clustering Approach

Abstract

Chronicles are temporal patterns well suited for an abstract representation of the behavior of dynamic systems. For fault diagnosis, chronicles describe the nominal and faulty behaviors of the process. Powerful algorithms allow the recognition of chronicles in the flow of observations of the system and appropriate actions can be taken when a faulty situation is recognized. However, designing chronicles is not a trivial thing to do. The increasing complexity and capacity of data generation of highly-advanced processes cause the acquisition of a complete model difficult. This paper focuses on the problem of discovering chronicles that are representative of a system behavior from direct observations. A clustering approach to this problem is considered. The chronicle discovery algorithm proposed here designs chronicles with minimal knowledge of the system to diagnose. Furthermore, unprocessed data obtained directly from the system can be used in this clustering algorithm. Finally, the chronicle discovery algorithm proposed in this paper is illustrated on a sport performance monitoring device for a diagnosis of movement deviations in the temporal domain, in the event domain, or both, considered as faults for the athlete.
Fichier principal
Vignette du fichier
SAFE18_final.pdf (567.14 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01817529 , version 1 (18-06-2018)

Identifiers

  • HAL Id : hal-01817529 , version 1

Cite

Alexandre Sahuguède, Euriell Le Corronc, Marie-Véronique V Le Lann. Chronicle Discovery for Diagnosis from Raw Data: A Clustering Approach. 10th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2018, Aug 2018, Varsaw, Poland. 8p. ⟨hal-01817529⟩
50 View
5 Download

Share

Gmail Facebook X LinkedIn More