Search

The turbulent nature of the marine atmospheric boundary layer and interactions across the air‐sea interface cause ever‐changing environmental conditions, including atmospheric properties that affect the index of refraction, or atmospheric refractivity. Variations in atmospheric refractivity lead to many types of anomalous propagation phenomena of electromagnetic (EM) signals; thus, improving performance of EM systems requires in situ knowledge of the refractivity. Inversion approaches to estimate refractivity rely on measured EM data; however, despite its importance, few studies have examined the influence of data density on the accuracy of refractivity inversions. This study applies a bistatic radar data inversion process to estimate atmospheric refractivity parameters in evaporative ducting conditions and examines the impacts of the sampling density of radar propagation loss data, and its source location, on accuracy of refractivity inversions. Genetic algorithms and a radar propagation model are used to perform the inversions. Numerical experiments examine various randomly distributed amounts of synthetic data from a 100‐m (altitude) by 60‐km (range) area. Three domains within this area are examined from which data were sourced. A data density of approximately 1% of the prediction domain yielded the smallest errors of refractivity parameters, and root mean square errors of refractivity and propagation loss. These error reductions are attributed to avoidance of nonunique solutions that likely impact lower data densities, supported by their classification as a misleading or difficult inverse problem. Generally, data sourced from long range result in lower refractivity and propagation loss root mean square errors compared to data sourced from other domains. Key Points: The density of data, randomly sampled within different domains, is varied to examine its affect on accuracy of refractivity inversionsRefractivity inversion accuracy is evaluated by errors in refractivity parameters, and root mean square errors of refractivity and propagationData density of approximately 1% of the propagation prediction space yielded the smallest refractivity errors and differences in propagation [ABSTRACT FROM AUTHOR]

The covariance and spectral characteristics of periodically correlated random processes (PCRP) are used to describe the state of rotary mechanical systems and in their fault detection. The methods for estimation of mean function, covariance function, instantaneous spectral density and their Fourier coefficients for a given class of non-stationary random processes on the basis of experimental data, namely: the synchronous averaging, component, least squares method and linear filtration methods are considered. The first and second order periodicity detection methods are used for vibration signals analysis. A method for mechanical system fault identification and classification based on a harmonic series representation is developed. Examples of fault detection in rolling/sliding bearings and gearboxes are given. [ABSTRACT FROM AUTHOR]

We discuss the use of the Hilbert transform for the analysis of periodically non-stationary random signals (PNRSs), whose carrier harmonics are modulated by jointly stationary high-frequency narrow-band random processes. PNRS of this type are suitable models for numerous natural and man-made phenomena, including the vibration of a damaged mechanism. We show that the auto-covariance function of the signal and its Hilbert transform are the same, and that their cross-covariance functions differ only in their sign, meaning that the sum of squares of the signal and its Hilbert transform cannot be considered a 'squared envelope' and no new information is contained compared with the variance of the raw signal. A representation of the signal in the form of a superposition of high-frequency components is obtained and it is shown that these components are jointly periodically non-stationary random processes. The properties of the band-pass filtered signals are examined, and it is shown that band-pass filtering can reduce both the number of signal variance cyclic harmonics and their amplitudes. We show that it is possible to extract the quadratures of narrow-band high-frequency modulation processes using the Hilbert transform. The results obtained here theoretically substantiate the use of the Hilbert transform for the analysis of high-frequency modulation which occurs when a fault appears. They offer a new way to consider the traditional approach to vibration diagnosis. A processing technique that can be considered an alternative to envelope analysis is described, and its use in the analysis of a vibration signal is discussed., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Roman Yuzefovych reports was provided by Karpenko Physico-mechanical institute of NAS of Ukraine. Roman Yuzefovych reports a relationship with Karpenko Physico-mechanical institute of NAS of Ukraine that includes: employment., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

It is shown that the models of gear pair vibration, proposed in literature, are particular cases of the bi-periodically correlated random processes (BPCRPs), which describe its stochastic recurrence with two periods. The possibility of vibration and analysis within the framework of BPCRP approximation, in the form of periodically correlated random processes (PCRPs), is grounded and the implementation of vibration processing procedures using PCRP techniques, which are worked out by the authors, is given. Searching for hidden periodicities of the first and the second orders was considered as the main issue of this approach. The estimation of the non-stationary period (basic frequency) allowed us to carry out a detailed analysis of the deterministic part, the covariance structure of the stochastic part, and to form, using their parameters, the sensitive indicators for fault detection. The results of the processing of the wind turbine gearbox vibration signals are presented. The amplitude spectra of the deterministic oscillations and the time changes of the stochastic part power for different fault stages are analyzed. The most efficient indicators, which are formed using the amplitude spectra for practical applications, are proposed. The presented approach was compared with known in literature cyclostationary analysis and envelope techniques, and its advantages are shown.