scispace - formally typeset
Search or ask a question
Author

Jan Helsen

Bio: Jan Helsen is an academic researcher from Vrije Universiteit Brussel. The author has contributed to research in topics: Turbine & Wind power. The author has an hindex of 15, co-authored 90 publications receiving 857 citations. Previous affiliations of Jan Helsen include VU University Amsterdam & Katholieke Universiteit Leuven.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, the gearbox modal behaviour assessment by means of three more complex modelling techniques of varying complexity is discussed. And the authors define two new mode categories: the planet carrier modes and planetary ring modes, and investigate the interaction between the structural modes of the planetary carrier and ring flexibility with the overall gearbox modes.

127 citations

Journal ArticleDOI
TL;DR: A general overview of the available knowledge regarding vibration-based speed estimation techniques is targeted by means of a performance comparison of seven speed estimation methods on three different experimental data sets and the resulting speed estimation data of all tested methods is made publicly available such that it can help in forming a benchmark for futurespeed estimation methods.

90 citations

Journal ArticleDOI
TL;DR: In this article, the authors focus on separating the bearing fault signals from masking signals coming from drivetrain elements like gears or shafts, which can be classified as cyclostationary.

85 citations

Journal ArticleDOI
TL;DR: The proposed approach is a simple yet effective way of tracking faults with a cyclostationary signature and key in the iterative optimization procedure is the usage of the Rayleigh quotient to update the filter coefficients.

85 citations


Cited by
More filters
Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

Journal ArticleDOI
TL;DR: This paper reviews the recent literature on machine learning models that have been used for condition monitoring in wind turbines and shows that most models use SCADA or simulated data, with almost two-thirds of methods using classification and the rest relying on regression.

482 citations

Journal ArticleDOI
TL;DR: In this paper, a detailed literature review focuses on dynamics-based gearbox fault modeling, detection and diagnosis, focusing on the following fundamental yet key aspects: gear mesh stiffness evaluation, gearbox damage modeling and fault diagnosis techniques, and gearbox transmission path modeling and method validation.

315 citations

Journal ArticleDOI
TL;DR: A systemic and pertinent state-of-art review on WT planetary gearbox condition monitoring techniques on the topics of fundamental analysis, signal processing, feature extraction, and fault detection is provided.

312 citations