Rolling bearing fault diagnosis based on EWT and ELM
نویسندگان
چکیده
منابع مشابه
Fault Diagnosis of Rolling Bearings Based on EWT and KDEC
Abstract: This study proposes a novel fault diagnosis method that is based on empirical wavelet transform (EWT) and kernel density estimation classifier (KDEC), which can well diagnose fault type of the rolling element bearings. With the proposed fault diagnosis method, the vibration signal of rolling element bearing was firstly decomposed into a series of F modes by EWT, and the root mean squa...
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Motor systems are very important in modern society. They convert almost 60% of the electricity produced in the U.S. into other forms of energy to provide power to other equipment. In the performance of all motor systems, bearings play an important role. Many problems arising in motor operations are linked to bearing faults. In many cases, the accuracy of the instruments and devices used to moni...
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A classification technique using Support Vector Machine (SVM) classifier for detection of rolling element bearing fault is presented here. The SVM was fed from features that were extracted from of vibration signals obtained from experimental setup consisting of rotating driveline that was mounted on rolling element bearings which were run in normal and with artificially faults induced conditio...
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Bearing failure is one of the dominant causes of failure and breakdowns in rotating machinery, leading to huge economic loss. Aiming at the nonstationary and nonlinear characteristics of bearing vibration signals as well as the complexity of condition-indicating information distribution in the signals, a novel rolling element bearing fault diagnosis method based on multifractal theory and gray ...
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ژورنال
عنوان ژورنال: Vibroengineering PROCEDIA
سال: 2018
ISSN: 2345-0533,2538-8479
DOI: 10.21595/vp.2018.20163