Statistična analiza vibracij in diagnostika kompresorjev hladilnih aparatov

magistrsko delo magistrskega študijskega programa II. stopnje Strojništvo

Blaž Judež (Author), Edvard Govekar (Mentor), Primož Potočnik (Co-mentor)

Abstract

V magistrskem delu obravnavamo problematiko zaznavanja neustrezno delujočih kompresorjev v hladilno zamrzovalnih aparatih. Kompresorje različnih vrst in proizvajalcev analiziramo in primerjamo med seboj na podlagi statističnih značilk v časovni in frekvenčni domeni. Značilke izpeljemo na podlagi statistične analize signalov vibracij delujočega kompresorja, ki jih pomerimo z merilnikom pospeška. Za zaznavanje neustreznih kompresorjev uvedemo odločitvene pragove, ki jih nastavimo na podlagi akustične presoje ene vrste kompresorjev. Odločitvene pragove nato statistično analiziramo ter podamo priporočila za njihovo uporabo na celotni populaciji kompresorjev.

Keywords

magistrske naloge;hladilno zamrzovalni aparati;kompresorji;zaznavanje napak;statistična analiza;izpeljava značilk;odločitveni pragovi;

Data

Language:	Slovenian
Year of publishing:	2019
Typology:	2.09 - Master's Thesis
Organization:	UL FS - Faculty of Mechanical Engineering
Publisher:	[B. Judež]
UDC:	621.51:621.56/.59(043.2)
COBISS:	16678683
Views:	1025
Downloads:	233
Average score:	0 (0 votes)
Metadata:

Other data

Secondary language:	English
Secondary title:	Statistical vibration analysis and diagnostics of compressors in refrigerating appliances
Secondary abstract:	In the master's thesis we consider the problem of detecting inadequately operating compressors in refrigerating and freezing appliances. Compressors of various types and manufacturers are analysed and compared to each other on the basis of statistical features in the time and frequency domain. We extract features based on statistical analysis of vibration signals of the operating compressor, which is measured with an accelerometer. To detect inadequate compressors we introduce the decision thresholds, which are set based on the acoustic evaluation of one type of compressor. Then we analyse the decision thresholds and provide recommendations for their application on the entire compressor population.
Secondary keywords:	refrigerating and freezing appliances;compressors;fault;detection;statistical analysis;features extraction;decision thresholds;
Type (COBISS):	Master's thesis/paper
Study programme:	0
Embargo end date (OpenAIRE):	1970-01-01
Thesis comment:	Univ. Ljubljana, Fak. za strojništvo
Pages:	XXII, 68 str.
ID:	11162289