Author
Radoslaw Zimroz
Other affiliations: University of Wrocław, Cranfield University
Bio: Radoslaw Zimroz is an academic researcher from Wrocław University of Technology. The author has contributed to research in topics: Condition monitoring & Signal. The author has an hindex of 32, co-authored 228 publications receiving 3724 citations. Previous affiliations of Radoslaw Zimroz include University of Wrocław & Cranfield University.
Papers published on a yearly basis
Papers
More filters
TL;DR: Bartelmus et al. as mentioned in this paper introduced a new diagnostic feature, which can be used for monitoring the condition of planetary gearboxes in time-variable operating conditions, which exploits the fact that a bad condition is more susceptible (yielding) to load than the gearbox in good condition.
296 citations
TL;DR: In this paper, it is shown that the most important factor of the proper planetary gearbox condition is connected with perturbation of arm rotation, where an arm rotation gives rise to a specific vibration signal whose properties are depicted by a short-time Fourier transform and Wigner-Ville distribution presented as a time-frequency map.
249 citations
TL;DR: In this paper, an EMD-based rolling bearing diagnosing method was proposed for bearing damage detection at a much earlier stage of damage development, by using EMD a raw vibration signal is decomposed into a number of Intrinsic Mode Functions ( IMF s) and then, a new method of IMF s aggregation into three Combined Mode Function (CMF s) was applied and finally the vibration signal was divided into three parts of signal.
175 citations
TL;DR: In this paper, a diagnostic approach is proposed for bearing used in wind turbines, which uses parameters obtained from commercial diagnostic system (peak-to-peak and root mean square (RMS) of vibration acceleration, and generator power that is related to the operating conditions).
157 citations
TL;DR: In this paper, two models of gearboxes (a fixed-axis two-stage gearbox and a planetary gearbox) operating under varying load conditions are proposed, and an original transmission error function expressing changes in technical condition and load variation is presented.
Abstract: Fault detection and diagnosis in mechanical systems during their time-varying nonstationary operation is one of the most challenging issues. In the last two decades or so researches have noticed that machines work in nonstationary load/speed conditions during their normal operation. Diagnostic features for gearboxes were found to be load dependent. This was experimentally confirmed by a smearing effect in the spectrum. In order to better understand the involved phenomena and to ensure agreement between simulation and experimental results, two models of gearboxes (a fixed-axis two-stage gearbox and a planetary gearbox) operating under varying load conditions are proposed. The models are based on two mechanical systems used in the mining industry, i.e. the belt conveyor and the bucket wheel excavator. An original transmission error function expressing changes in technical condition and load variation is presented. Energy based parameters (the signal RMS value or the arithmetic sum of the amplitudes of spectral gearmesh components) are adopted as the diagnostic features. Simulation results show a strong correlation between load values, changes in condition and the diagnostic features. The findings are key to condition monitoring. Thanks to the use of the models one can better understand the phenomena identified through an analysis of vibration signals captured from real machines.
151 citations
Cited by
More filters
[...]
TL;DR: There is, I think, something ethereal about i —the square root of minus one, which seems an odd beast at that time—an intruder hovering on the edge of reality.
Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.
Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …
33,785 citations
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
2,345 citations
TL;DR: This paper attempts to survey and summarize the recent research and development of EMD in fault diagnosis of rotating machinery, providing comprehensive references for researchers concerning with this topic and helping them identify further research topics.
1,410 citations