diplomsko delo
Abstract
Stroji in naprave v današnjem času proizvajajo velike količine podatkov, ki se navadno prenašajo in hranijo na oddaljenih strežnikih. To lahko prinaša velike stroške prenosa in hrambe podatkov ter zakasnitve, postavlja pa tudi vprašanja o varnosti in zasebnosti. Obdelava podatkov na robu omrežja omogoča, da podatke senzorjev in naprav analiziramo ter obdelamo v njihovi neposredni bližini, v oblak pa pošljemo samo ključne informacije, ki služijo nadaljnji obdelavi.
Diplomsko delo opisuje praktičen primer spremljanja učinkovitosti proizvodnega procesa z izračunom kazalnikov skupne učinkovitosti opreme (OEE) na robu omrežja. V okolju TwinCAT poteka simulacija proizvodne linije, ki temelji na modelu stanj PackML. Naprava TwinCAT je povezana z računalnikom Raspberry Pi, na katerem teče program, napisan v programskem jeziku Python, ki zajema podatke iz proizvodnega procesa in v realnem času izračunava kazalnike OEE. Ti se nato prenašajo v spletno aplikacijo Azure IoT Central, kjer ima operater oddaljen nadzor nad učinkovitostjo procesa.
Keywords
podatki;proizvodnja;internet stvari;skupna učinkovitost opreme;TwinCAT;PackML;Azure IoT;Python;Raspberry Pi;visokošolski strokovni študij;Aplikativna elektrotehnika;diplomske naloge;
Data
Language: |
Slovenian |
Year of publishing: |
2021 |
Typology: |
2.11 - Undergraduate Thesis |
Organization: |
UL FE - Faculty of Electrical Engineering |
Publisher: |
[J. Špeh] |
UDC: |
004.6/.7:658.5(043.2) |
COBISS: |
68979203
|
Views: |
493 |
Downloads: |
151 |
Average score: |
0 (0 votes) |
Metadata: |
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Other data
Secondary language: |
English |
Secondary title: |
Production process efficiency monitoring with edge computing |
Secondary abstract: |
Machines and devices nowadays produce large amounts of data that are typically
transmitted and stored on remote servers. This can lead to high data transfer
and storage costs, delays, as well as security and privacy issues. Data processing
at the edge of the network allows the data of sensors and devices to be analyzed
and processed in their immediate proximity, and only key information is sent to
the cloud for further processing.
The thesis describes a practical example of monitoring the effectiveness of
the production process by calculating the overall equipment effectiveness (OEE)
indicators at the edge of the network. The TwinCAT environment runs a production
line simulation based on the PackML state model. The TwinCAT device is
connected to a Raspberry Pi computer running a program written in the Python
programming language, which captures data from the production process and
calculates OEE indicators in real-time. These are then transferred to the Azure
IoT Central web application, where the operator has remote supervisory control
over the effectiveness of the process. |
Secondary keywords: |
data;production;internet of things;overall equipment effectiveness;TwinCAT;PackML;Azure IoT;Python;Raspberry Pi; |
Type (COBISS): |
Bachelor thesis/paper |
Study programme: |
1000315 |
Embargo end date (OpenAIRE): |
1970-01-01 |
Thesis comment: |
Univ. v Ljubljani, Fak. za elektrotehniko |
Pages: |
XVIII, 75 str. |
ID: |
13059372 |