Removal of transfer function effects from gear vibration signals under constant and variable speed conditions

Lu, Runyu; Borghesani, Pietro; Randall, R.B.; Smith, Wade A.; Peng, Zhongxiao

doi:10.1016/j.ymssp.2022.109714

Cited by 7 publications

(2 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are, of course, more approaches to the diagnosis of gears and roller bearings presented in various papers [15,19,49]. More examples, which will help interested readers deepen their learning on the subject, can be found in the references [15,17,50,51] for bearings and in the references [52][53][54] for gear diagnosis.…”

Section: Gear and Bearing Diagnosismentioning

confidence: 99%

Signal Processing for the Condition-Based Maintenance of Rotating Machines via Vibration Analysis: A Tutorial

Matania,

Bachar,

Bechhoefer

et al. 2024

Sensors

View full text Add to dashboard Cite

One of the common methods for implementing the condition-based maintenance of rotating machinery is vibration analysis. This tutorial describes some of the important signal processing methods existing in the field, which are based on a profound understanding of the component’s physical behavior. Furthermore, this tutorial provides Python and MATLAB code examples to demonstrate these methods alongside explanatory videos. The goal of this article is to serve as a practical tutorial, enabling interested individuals with a background in signal processing to quickly learn the important principles of condition-based maintenance of rotating machinery using vibration analysis.

show abstract

Section: Gear and Bearing Diagnosismentioning

confidence: 99%

Signal Processing for the Condition-Based Maintenance of Rotating Machines via Vibration Analysis: A Tutorial

Matania,

Bachar,

Bechhoefer

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…It is usual to separate deterministic and stochastic sources for independent analysis within damage diagnosis procedures [4]. Typical methods include cepstral editing, wavelet and other signal decompositions, like the empirical mode decomposition [1,2,4]. Supervised methods can also be used to guide the signal decomposition or feature extraction procedure [5].…”

Section: Introductionmentioning

confidence: 99%

A Fast Identification Algorithm for Linear Parameter Varying Vector Ar Models of Short-Term Drivetrain Vibration

Avendaño-Valencia,

Georgantopoulou

2023

10th ECCOMAS Thematic Conference on Smart Structures and Materials

View full text Add to dashboard Cite

Wind turbine drivetrain vibrations are characterised by very complex dynamics originating from the convolution of deterministic and stochastic excitation sources moving through the structural dynamics of the wind turbine. In this work we postulate Linear Parameter Varying Vector AutoRegressive (LPV-VAR) models to represent those signals. To deal with the complexity of these models during the identification procedure, we devise an algorithm, based on the QR decomposition of the regression matrix, to accelerate the model identification procedure. The proposed methods are demonstrated on data from a wind turbine drivetrain simulator.

show abstract

Estimating vibration sources for industrial IoT using dilated CNN and deconvolution

Makienko,

Grebshtein,

Gildish

2024

Internet of Things

View full text Add to dashboard Cite

Removal of transfer function effects from gear vibration signals under constant and variable speed conditions

Cited by 7 publications

References 38 publications

Signal Processing for the Condition-Based Maintenance of Rotating Machines via Vibration Analysis: A Tutorial

Signal Processing for the Condition-Based Maintenance of Rotating Machines via Vibration Analysis: A Tutorial

A Fast Identification Algorithm for Linear Parameter Varying Vector Ar Models of Short-Term Drivetrain Vibration

Estimating vibration sources for industrial IoT using dilated CNN and deconvolution

Contact Info

Product

Resources

About