Articles with "element bearings" as a keyword



CARLE: a hybrid deep-shallow learning framework for robust and explainable RUL estimation of rolling element bearings

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Published in 2025 at "Soft Computing"

DOI: 10.1007/s00500-025-10937-w

Abstract: Prognostic health management (PHM) systems have extensive applications in industry for monitoring and predicting the health status of equipment. Remaining Useful Life (RUL) estimation stands out as one important part of a PHM system that… read more here.

Keywords: element bearings; rolling element; rul estimation; framework ... See more keywords

Fault Diagnosis Method for Rolling Element Bearings Under Variable Speed Based on TKEO and Fast-SC

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Published in 2018 at "Journal of Failure Analysis and Prevention"

DOI: 10.1007/s11668-018-0383-y

Abstract: Abstract With rotating speed of rotating machinery, it is difficult to maintain stability in practical work which brings many difficulties to the condition monitoring of rotating machinery. When rolling element bearings work under variable speed,… read more here.

Keywords: fault; variable speed; method; speed ... See more keywords

Novel ensemble techniques for classification of rolling element bearing faults

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Published in 2017 at "Journal of the Brazilian Society of Mechanical Sciences and Engineering"

DOI: 10.1007/s40430-016-0540-8

Abstract: Rolling element bearings are the critical mechanical components in industrial applications. These rotary elements work continuously under different operating and environmental conditions. It leads to generation of various defects over the operating surfaces of bearing… read more here.

Keywords: ensemble techniques; machine; classification; classification rolling ... See more keywords
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Fault diagnosis of rolling element bearings based on Multiscale Dynamic Time Warping

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Published in 2017 at "Measurement"

DOI: 10.1016/j.measurement.2016.10.038

Abstract: Abstract A fault recognition method for rolling element bearings based on Multiscale Dynamic Time Warping (MDTW) is proposed in this paper. After preprocessing using Empirical Mode Decomposition (EMD), CWs are extracted from vibration signals. The… read more here.

Keywords: based multiscale; bearings based; rolling element; element bearings ... See more keywords

Two novel mixed effects models for prognostics of rolling element bearings

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Published in 2018 at "Mechanical Systems and Signal Processing"

DOI: 10.1016/j.ymssp.2017.06.004

Abstract: Abstract Rolling element bearings are widely used in various machines to support rotating shafts. Due to harsh working environments, the health condition of a bearing degrades over time. A typical bearing degradation process includes two… read more here.

Keywords: two mixed; error; mixed effects; element bearings ... See more keywords
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A comprehensive evaluation of the effect of defect size in rolling element bearings on the statistical features of the vibration signal

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Published in 2021 at "Mechanical Systems and Signal Processing"

DOI: 10.1016/j.ymssp.2020.107334

Abstract: Abstract A defective rolling element bearing produces a specific vibration pattern that can be used as a diagnostic tool in predictive maintenance. Defect growth from an incipient fault to a fully developed fault alters the… read more here.

Keywords: defect size; statistical features; rolling element; element bearings ... See more keywords
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A Methodology to Measure Power Losses of Rolling Element Bearings under Combined Radial and Axial Loading Conditions

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Published in 2021 at "Tribology Transactions"

DOI: 10.1080/10402004.2021.1937760

Abstract: In this study, an experimental methodology is presented for measuring power losses of rolling element bearings under combined radial and axial loading conditions. The set-up developed for this purp... read more here.

Keywords: methodology; power losses; bearings combined; element bearings ... See more keywords

Local mean decomposition enhanced graph spectrum analysis for condition monitoring of rolling element bearings

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Published in 2025 at "Measurement Science and Technology"

DOI: 10.1088/1361-6501/ada632

Abstract: Early fault detection and diagnosis of rolling element bearings (REBs) is crucial for preventing unexpected machine failures, thereby ensuring operational reliability and lowering maintenance costs. Component signals decomposed by local mean decomposition (LMD) are effective… read more here.

Keywords: graph spectrum; spectrum; rolling element; fault detection ... See more keywords

Adaptive Multipoint Optimal Minimum Entropy Deconvolution Adjusted and Application to Fault Diagnosis of Rolling Element Bearings

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Published in 2019 at "IEEE Sensors Journal"

DOI: 10.1109/jsen.2019.2937140

Abstract: Extracting fault-related features from noisy vibration signals is a pivotal prerequisite for condition monitoring of rolling element bearings. Multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is a non-iterative blind deconvolution algorithm and has been proven… read more here.

Keywords: multipoint optimal; deconvolution; period; rolling element ... See more keywords

Application of Feature Fusion Using Coaxial Vibration Signal for Diagnosis of Rolling Element Bearings

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Published in 2020 at "Shock and Vibration"

DOI: 10.1155/2020/8831723

Abstract: The research of rolling element bearings (REBs) fault diagnosis based on single sensor vibration signal analysis is very common. However, the information provided by an individual sensor is very limited, and the robustness of the… read more here.

Keywords: diagnosis; vibration; coaxial vibration; element bearings ... See more keywords

A probabilistic approach to remaining useful life prediction of rolling element bearings

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Published in 2019 at "Structural Health Monitoring"

DOI: 10.1177/1475921718758517

Abstract: In this article, we present a probabilistic approach for fault detection and prognosis of rolling element bearings based on a two-phase degradation model. One of the main issues in dealing with bearing degradation is that… read more here.

Keywords: degradation; methodology; element bearings; approach ... See more keywords