Induction machine fault detection using smartphone recorded audible noise

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F18%3A43951169" target="_blank" >RIV/49777513:23220/18:43951169 - isvavai.cz</a>

Výsledek na webu

<a href="http://digital-library.theiet.org/content/journals/10.1049/iet-smt.2017.0104?originator=ietauthorOffprint&identity=412184&timestamp=20190221141025&signature=d8043cbc701f6251d181c0efbfc2773e&tinyUrl=http://ietdl.org/t/sPtbv" target="_blank" >http://digital-library.theiet.org/content/journals/10.1049/iet-smt.2017.0104?originator=ietauthorOffprint&identity=412184&timestamp=20190221141025&signature=d8043cbc701f6251d181c0efbfc2773e&tinyUrl=http://ietdl.org/t/sPtbv</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1049/iet-smt.2017.0104" target="_blank" >10.1049/iet-smt.2017.0104</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Induction machine fault detection using smartphone recorded audible noise

Popis výsledku v původním jazyce

This study presents induction machine fault detection possibilities using smartphone recorded audible noise. Acoustic and audible noise analysis for fault detection is a well-established technique; however, specialised equipment for diagnostic purposes is often very expensive and difficult to operate. To overcome this obstacle, a simple pre-diagnostic procedure, using hand-held smartphones is proposed. Different faults of the three-phase squirrel cage induction machine such as various numbers of broken rotor bars and dynamic rotor eccentricity are inflicted to the machine and the resulting audible signals are recorded in laboratory circumstances using two widely available commercial smartphones. The analysis is performed on audible noise and compared with the results of mechanical vibrations measurements, recorded by vibration sensors. Rotational speed frequency and twice-line frequency are used as diagnostic indicators of faults. A simple neural network is composed and probabilities of fault detection using such diagnostic measures are presented. The necessity for further study as well as further implementation and method refinement necessity is pointed out.

Název v anglickém jazyce

Induction machine fault detection using smartphone recorded audible noise

Popis výsledku anglicky

This study presents induction machine fault detection possibilities using smartphone recorded audible noise. Acoustic and audible noise analysis for fault detection is a well-established technique; however, specialised equipment for diagnostic purposes is often very expensive and difficult to operate. To overcome this obstacle, a simple pre-diagnostic procedure, using hand-held smartphones is proposed. Different faults of the three-phase squirrel cage induction machine such as various numbers of broken rotor bars and dynamic rotor eccentricity are inflicted to the machine and the resulting audible signals are recorded in laboratory circumstances using two widely available commercial smartphones. The analysis is performed on audible noise and compared with the results of mechanical vibrations measurements, recorded by vibration sensors. Rotational speed frequency and twice-line frequency are used as diagnostic indicators of faults. A simple neural network is composed and probabilities of fault detection using such diagnostic measures are presented. The necessity for further study as well as further implementation and method refinement necessity is pointed out.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

IET Science Measurement &amp; Technology

ISSN

1751-8822

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

7

Strana od-do

554-560

Kód UT WoS článku

000435560400020

EID výsledku v databázi Scopus

2-s2.0-85048939318