eprintid: 23714
rev_number: 11
eprint_status: archive
userid: 3423
dir: disk0/00/02/37/14
datestamp: 2017-11-23 10:40:08
lastmod: 2017-12-21 08:50:55
status_changed: 2017-11-23 10:40:08
type: doctoralThesis
metadata_visibility: show
creators_name: Größl, Martin
title: Konzeptioneller Ansatz einer Fehlerprädiktionsumgebung
divisions: 110001
adv_faculty: af-11
abstract: In this thesis a failure prediction environment based on selected methods of statistics, machine learning and data mining was developed. For the system behavior representation of an observed information system mathematical models,such as Bayesian networks and Markov chains, were generated. These models were analyzed using a probabilistic model-checking for both the process evolution (path) into a fault condition as well as the time duration until the occurrence
of this possible fault.
Furthermore, based on a Kalman filter an approach for identification of anomalies and / or misbehavior in data streams was developed. This also includes a system identification part, which derives the models from measurement data.
These models form the basis for the misbehavior detection.
date: 2017
id_scheme: DOI
id_number: 10.11588/heidok.00023714
ppn_swb: 1657340171
own_urn: urn:nbn:de:bsz:16-heidok-237144
date_accepted: 2017-09-27
advisor: HASH(0x556120a04928)
language: ger
bibsort: GROSSLMARTKONZEPTION2017
full_text_status: public
citation:   Größl, Martin  (2017) Konzeptioneller Ansatz einer Fehlerprädiktionsumgebung.  [Dissertation]     
document_url: https://archiv.ub.uni-heidelberg.de/volltextserver/23714/1/Phd_Thesis_Martin_Groessl_UNI.pdf