M
Marc Tomczak
Researcher at Nancy-Université
Publications - 15
Citations - 192
Marc Tomczak is an academic researcher from Nancy-Université. The author has contributed to research in topics: Decimation & Matrix pencil. The author has an hindex of 6, co-authored 15 publications receiving 188 citations.
Papers
More filters
Journal ArticleDOI
A method for analysing gearbox faults using time–frequency representations
TL;DR: In this paper, a three-step TF analysis method is introduced, consisting of calculation of the TF representation, physical interpretation of the main components observed, and model building and validation.
Journal ArticleDOI
Gear crack detection by adaptive amplitude and phase demodulation
TL;DR: In this article, a new method for gear crack detection is presented, which consists of the coupling of adaptive demodulation with an abrupt change detector, which is intended to account for the slow variations of the signal.
Journal ArticleDOI
Perturbation Analysis of Subspace-Based Methods in Estimating a Damped Complex Exponential
El-Hadi Djermoune,Marc Tomczak +1 more
TL;DR: First-order perturbation analysis is used to derive closed-form expressions of the variance of the complex mode, frequency and damping factor estimates for three SVD-based estimation methods in the case of a single-mode damped exponential.
Journal ArticleDOI
A subband ARMA modeling approach to high-resolution NMR spectroscopy
Marc Tomczak,El-Hadi Djermoune +1 more
TL;DR: In this paper, a low numerical complexity method for parameter estimation of damped exponential signals is proposed, which allows one to handle with free induction decay (FID) signals of high complexity.
Journal ArticleDOI
A new adaptive subband decomposition approach for automatic analysis of NMR data.
TL;DR: This paper presents a non-iterative, fast, and almost automated time-data analysis method for NMR spectroscopy, based on a new adaptive implementation of high resolution methods used in spectral subbands, which allows one to obtain a better detection rate at a lower complexity comparatively to other stopping rules, while preserving a reasonable estimation variance.