58 citations · 298 across the 19 of their papers we have counts for
19 papers
A method for signal components identification in acoustic signal with non-Gaussian background noise using clustering of data in time-frequency domain
A Drewnicka, A Michalak, R Zimroz +3
This paper presents a novel method for fault detection in vibration/acoustic signals contaminated with non-Gaussian noise, specifically addressing the challenge of random impulsive…
Robust correlation measures for informative frequency band selection in heavy-tailed vibration signal
Justyna Hebda-Sobkowicz, Radosław Zimroz, Anil Kumar +1
Vibration signals are commonly used to detect local damage in rotating machinery. However, raw signals are often noisy, particularly in crusher machines, where the technological pr…
Applications of robust statistics for cyclostationarity detection in non-Gaussian signals for local damage detection in bearings
Wojciech Żuławiński, Jérôme Antoni, Radosław Zimroz +1
Signals with periodic characteristics are ubiquitous in real-world applications. One of these areas is condition monitoring, where the vibration signals from rotating machines natu…
Identification of fault frequency variation in the envelope spectrum in the vibration-based local damage detection in possible changing load/speed conditions
Daniel Kuzio, Radosław Zimroz, Agnieszka Wyłomańska
The problem of local damage diagnosis (based on the detection of impulsive and periodic signals) is discussed. Both features should be checked, as fault frequency must be linked to…
Bearing damage detection with orthogonal and non-negative low-rank feature extraction
Mateusz Gabor, Rafal Zdunek, Agnieszka Wylomanska +1
Local damage of bearings can be detected as a weak cyclic and impulsive component in a highly noisy measured signal. A key problem is how to extract the signal of interest (SOI) fr…
Non-negative tensor factorization for vibration-based local damage detection
Mateusz Gabor, Rafal Zdunek, Radoslaw Zimroz +2
In this study, a novel non-negative tensor factorization (NTF)-based method for vibration-based local damage detection in rolling element bearings is proposed. As the diagnostic si…
Non-negative matrix underapproximation as optimal frequency band selector
Mateusz Gabor, Rafal Zdunek, Radoslaw Zimroz +1
Time-frequency representation (TFR) is often used for non-stationary signal analysis. The most intuitive and interpretable TFR is the spectrogram. Recently, a concept of non-negati…
Assessment of background noise properties in time and time-frequency domains in the context of vibration-based local damage detection in real environment
Katarzyna Skowronek, Tomasz Barszcz, Jerome Antoni +2
Any measurement in condition monitoring applications is associated with disturbing noise. Till now, most of the diagnostic procedures have assumed the Gaussian distribution for the…
The modified Yule-Walker method for multidimensional infinite-variance periodic autoregressive model of order 1
Prashant Giri, Aleksandra Grzesiek, Wojciech Żuławiński +2
The time series with periodic behavior, such as the periodic autoregressive (PAR) models belonging to the class of the periodically correlated processes, are present in various rea…
Identification and validation of periodic autoregressive model with additive noise: finite-variance case
Wojciech Żuławiński, Aleksandra Grzesiek, Radosław Zimroz +1
In this paper, we address the problem of modeling data with periodic autoregressive (PAR) time series and additive noise. In most cases, the data are processed assuming a noise-fre…
Memory-multi-fractional Brownian motion with continuous correlations
Wei Wang, Michal Balcerek, Krzysztof Burnecki +6
We propose a generalization of the widely used fractional Brownian motion (FBM), memory-multi-FBM (MMFBM), to describe viscoelastic or persistent anomalous diffusion with time-depe…
The expectation-maximization algorithm for autoregressive models with normal inverse Gaussian innovations
Monika S. Dhull, Arun Kumar, Agnieszka Wylomanska
The autoregressive (AR) models are used to represent the time-varying random process in which output depends linearly on previous terms and a stochastic term (the innovation). In t…
Leveraging large-deviation statistics to decipher the stochastic properties of measured trajectories
Samudrajit Thapa, Agnieszka Wyłomańska, Grzegorz Sikora +5
Extensive time-series encoding the position of particles such as viruses, vesicles, or individual proteins are routinely garnered in single-particle tracking experiments or superco…
Large deviations of time-averaged statistics for Gaussian processes
J. Gajda, A. Wylomanska, H. Kantz +2
In this paper we study the large deviations of time averaged mean square displacement (TAMSD) for Gaussian processes. The theory of large deviations is related to the exponential d…
Probabilistic properties of detrended fluctuation analysis for Gaussian processes
G. Sikora, M. Hoell, A. Wylomanska +3
The detrended fluctuation analysis (DFA) is one of the most widely used tools for the detection of long-range correlations in time series. Although DFA has found many interesting a…
Elucidating distinct ion channel populations on the surface of hippocampal neurons via single-particle tracking recurrence analysis
Grzegorz Sikora, Agnieszka Wyłomańska, Janusz Gajda +4
Protein and lipid nanodomains are prevalent on the surface of mammalian cells. In particular, it has been recently recognized that ion channels assemble into surface nanoclusters i…
Fractional Brownian motion time-changed by gamma and inverse gamma process
A. Kumar, A. Wyłomańska, R. Połoczański +1
Many real time-series exhibit behavior adequate to long range dependent data. Additionally very often these time-series have constant time periods and also have characteristics sim…
Recognition of stable distribution with Levy index alpha close to 2
Krzysztof Burnecki, Agnieszka Wyłomańska, Aleksei Beletskii +2
We address the problem of recognizing alpha-stable Levy distribution with Levy index close to 2 from experimental data. We are interested in the case when the sample size of availa…
The dependence structure for PARMA models with alpha-stable innovations
Joanna Nowicka-Zagrajek, Agnieszka Wylomanska
In this paper we investigate the dependence structure for PARMA models (i.e. ARMA models with periodic coefficients) with symmetric alpha-stable innovations. In this case the covar…