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Open AccessArticle

Hybrid Model of Rolling-Element Bearing Vibration Signal

by Adam Jablonski

Energies 2022, 15(13), 4819; https://doi.org/10.3390/en15134819 - 30 Jun 2022

Cited by 6 | Viewed by 2423

The generation of synthetic vibration signals enables the testing of novel machine diagnostic methods without the costly introduction of real failures. One of major goals of vibration-based condition monitoring is the early detection of bearing faults. This paper presents a novel modeling technique based on the combination of the known mechanical properties of a modeled object (phenomenological part) and observation of a real object (behavioral part). The model uses the real pulse response of bearing housing, along with the external instantaneous machine speed profile. The presented method is object-oriented, so it is applicable to a large group of machinery. Full article

(This article belongs to the Special Issue Challenges and Research Trends of Identification of Mechanical Systems in the Energy Sector)

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18 pages, 5504 KB

Open AccessArticle

Short-Time/-Angle Spectral Analysis for Vibration Monitoring of Bearing Failures under Variable Speed

by Edgar F. Sierra-Alonso, Julian Caicedo-Acosta, Álvaro Ángel Orozco Gutiérrez, Héctor F. Quintero and German Castellanos-Dominguez

Appl. Sci. 2021, 11(8), 3369; https://doi.org/10.3390/app11083369 - 9 Apr 2021

Cited by 11 | Viewed by 3304

Abstract

Vibration-condition monitoring aims to detect bearing damages of rotating machinery’s incipient failures mainly through time–frequency methods because of their efficient analysis of nonstationary signals. However, by having failures with impulse behavior, short-term events have a tendency to be diluted under variable-speed conditions, while information on frequency changes tends to be lost. Here, we introduce an approach to highlighting bearing impulsive failures by measuring short-term spectral components to deal with variable-speed vibrations. The short-term estimator employs two sliding windows: a small one that measures the instantaneous amplitude level and tracks impulsive components and a large interval that evaluates the average background amplitude. Aiming to characterize cyclo-non-stationary processes with impulsive behavior, the emphasizing high-order-based estimator based on the principle of spectral entropy is introduced. For evaluation, both visual inspection and classifier performance are assessed, contrasting the spectral-entropy estimator with the widely used spectral-kurtosis approach for dealing with impulsive signals. The validation of short-time/-angle spectral analysis performed on three datasets at variable speed showed that the proposed spectral-entropy estimator is a promising indicator for emphasizing bearing failures with impulse behavior. Full article

(This article belongs to the Special Issue Bearing Fault Detection and Diagnosis)

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