Author
Mario Eltabach
Bio: Mario Eltabach is an academic researcher. The author has contributed to research in topics: Cyclostationary process & Autoregressive model. The author has an hindex of 4, co-authored 7 publications receiving 268 citations.
Papers
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TL;DR: A model of rotating machine signals is introduced which sheds light on the various components to be expected in the squared envelope spectrum, and a critical comparison is made of three sophisticated methods, namely, the improved synchronous average, the cepstrum prewhitening, and the generalized synchronousaverage, used for suppressing the deterministic part.
125 citations
TL;DR: It is shown that the optimal way of describing cyclo-non-stationary signals is jointly in the time and the angular domains and the related spectral representations, the order-frequency spectral correlation and coherence functions are proposed and their efficiency is demonstrated on two industrial cases.
83 citations
TL;DR: In this paper, a model-based technique for detecting wear in a multi-stage planetary gearbox used by lifting cranes is proposed, which combines cyclostationarity and autoregressive models.
70 citations
TL;DR: In this article, a generalized synchronous average (GSA) was proposed to extract the deterministic part of a cyclo-non-stationary vibration signal, i.e. the analog of the periodic part of cyclostationary signals.
50 citations
01 Jun 2016
5 citations
Cited by
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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
TL;DR: In this paper, the Fast Spectral Correlation (FSC) estimator is proposed, which is based on the short-time Fourier transform (STFT) for cyclostationary signals.
228 citations
TL;DR: In this paper, a reweighted singular value decomposition (RSVD) strategy is proposed for signal denoising and weak feature enhancement in a two-stage gearbox as well as train bearings.
219 citations
TL;DR: In this article, the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes, are reviewed.
Abstract: Large wind farms are gaining prominence due to increasing dependence on renewable energy. In order to operate these wind farms reliably and efficiently, advanced maintenance strategies such as condition based maintenance are necessary. However, wind turbines pose unique challenges in terms of irregular load patterns, intermittent operation and harsh weather conditions, which have deterring effects on life of rotating machinery. This paper reviews the state-of-the-art in the area of diagnostics and prognostics pertaining to two critical failure prone components of wind turbines, namely, low-speed bearings and planetary gearboxes. The survey evaluates those methods that are applicable to wind turbine farm-level health management and compares these methods on criteria such as reliability, accuracy and implementation aspects. It concludes with a brief discussion of the challenges and future trends in health assessment for wind farms.
163 citations
TL;DR: In this paper, a matching synchrosqueezing transform (MSST) was proposed to improve the readability of the TF representation of nonstationary signals composed of multiple components with slow varying instantaneous frequency (IF).
141 citations