The analysis of records of discrete events
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F19%3A00341068" target="_blank" >RIV/68407700:21730/19:00341068 - isvavai.cz</a>
Result on the web
<a href="https://www.ciirc.cvut.cz/cs/research-education/past-projects/damias/#sw1" target="_blank" >https://www.ciirc.cvut.cz/cs/research-education/past-projects/damias/#sw1</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
The analysis of records of discrete events
Original language description
The analysis is based on the search for a pattern of data obtained from a production process record, with data representing discrete events such as product creation, machine startup, processing error, etc. It is assumed that individual machines are involved in the production process (assets) and that the data obtained comes from many consecutive production runs. Realized result enables to search in the data frequent repeating patterns on individual machines. The patterns are subsequently hierarchically assigned across machine groups. As a result, information can beobtained from the data on which machines are involved in a single operation without any knowledge of the structure of the production line and the production process. The advantage of the algorithm is its linear complexity with respect to the length of the input sequence. The analysis results in a set of identified partial sequences. These are the sequences that have the highest score during detection, which is typically the coverage of the input sequence and the frequency of occurrence. Such sequences represent individual manufacturing or assembly recipes / manufacturing processes. Each sequence, or identified process, is described by statistical distribution and characteristics such as the mean duration value, the duration variance, and the start time variance of the sequence relative to the beginning of the entire production cycle. Based on these statistical parameters, it is possible to look for anomalies in the data, which may indicate an emerging or already existing error in the production process.
Czech name
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Czech description
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Classification
Type
R - Software
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/TF04000054" target="_blank" >TF04000054: DAMiAS - Data-driven Asset Management in Automobile Industry Based on Semantic Modelling</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Internal product ID
TF04000054
Technical parameters
set of Python libraries implementing the analysis Smlouva o využití výsledků projektu číslo TF04000054 s Factorio Solutions, s.r.o., se sídlem: U Louže 575, 25067 Klecany, Česká republika, IČO: 02673975, ze dne 30.1.2020 Odpovědná osoba za ČVUT, CIIRC je Ing. Pavel Burget, Ph.D.
Economical parameters
0,5 – 1 mil. Kč
Owner IČO
68407700
Owner name
Český institut informatiky, robotiky a kybernetiky