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20232025
most citedAn Adaptive feature mode decomposition based on a novel health indicator for bearing fault diagnosis

130 citations · 270 across the 11 of their papers we have counts for

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8 papers · 1 filter

eess.SP2025

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…

eess.SP2025

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…

eess.SP2024★ 5 cited

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…

eess.SP2024★ 130 cited

An Adaptive feature mode decomposition based on a novel health indicator for bearing fault diagnosis

Sumika Chauhan, Govind Vashishtha, Rajesh Kumar +2

The vibration analysis of the bearing is very crucial because of its non-stationary nature and low signal-to-noise ratio. Therefore, a novel scheme for detecting bearing defects is…

eess.SP2024★ 8 cited

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…

eess.SP2024★ 21 cited

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…

eess.SP2024

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…

eess.SP2023★ 59 cited

Non-parametric Ensemble Empirical Mode Decomposition for extracting weak features to identify bearing defects

Anil Kumar, Yaakoub Berrouche, Radosław Zimroz +5

A non-parametric complementary ensemble empirical mode decomposition (NPCEEMD) is proposed for identifying bearing defects using weak features. NPCEEMD is non-parametric because, u…