Dominant Frequency Components Research Articles

Time–frequency ridge characterisation of sleep stage transitions: Towards improving electroencephalogram annotations using an advanced visualisation technique

Manual sleep stage scoring of polysomnography recordings is an expensive and time-consuming process, further complicated by inconsistent sleep stage agreement among sleep experts (clinicians and sleep technologists). Hence, development of automated sleep scoring algorithms are an emerging topic of interest. Automation typically mimics the clinical decision path by implementing a series of predefined rules, such as the American Academy of Sleep Medicine’s (AASM) scoring manual. Recently, data driven methods have emerged using machine or deep learning. Both manual and automated methods of scoring have known limitations; primarily, unacceptable variation in agreement between different scorers and algorithms. Within the literature, electroencephalogram (EEG) frequency is an important feature considered by both sleep experts and automated approaches for classifying sleep stages.This study presents a novel approach to sleep stage analysis, by developing a methodology to precisely determine the temporal location of sleep stage transitions. The current gold standard fails to identify such transitional changes, which leads to poor inter-scorer reliability. Therefore, development and implementation of such methodologies is a crucial, but overlooked, step in improving the consistency of scoring within sleep studies. In this work, EEG time–frequency ridge analysis was used to characterise the dominant frequency component of EEG signals in time, at the point of sleep stage transition. An in-depth analysis of N3 → N2 and N2 → N3 transitions in the 2018 PhysioNet challenge “You Snooze, You Win” and the Wisconsin Sleep Cohort (WSC) datasets (n = 994, n = 742; approximately 13,888 h of sleep data) showed consistent time–frequency patterns at the point of transition, from one sleep stage to another. This methodology allows simple and ‘interpretable’ features to be generated in future work, to precisely identify the temporal location of sleep stage transitions with the aim of improving inter-scorer reliability.

Comparative Study on PSD and HHT Techniques for Condition Assessment of Steel Truss Bridge Using Acceleration Data

Bridge structures deteriorate over time due to factors like corrosion, fatigue, aging of materials, and unexpected natural or accidental events. Consequently, the vibrations measured in these structures exhibit time-varying and non-stationary characteristics. Additionally, non-linearities are inherent in these structures due to material behavior, boundary condition changes, and joints or connections. Analysis of non-linear and non-stationary structural vibration data is necessary to extract information on the condition of the bridge. Most methods assessing bridge conditions from acceleration responses rely on the Power Spectral Density (PSD) which is based on the Fourier Transform. However, the accuracy of the Fourier transforms for non-linear and non-stationary signals limits its application potential. The Hilbert–Huang Transform (HHT) has emerged as an alternative for handling these non-stationary signals due to its time-frequency-energy representation. This study provides a comparative study of both techniques by utilizing the data obtained from continuously monitored Pamban Bridge. This steel truss bridge was fitted with accelerometers to record bidirectional acceleration time histories at twenty bottom node points on the bridge. This study examines the continuous variations in the frequency component by both methods for 136 days. A linear best-fit trendline is obtained for the frequency parameters at different sensor positions. The slope and intercept values of this linear best fit trendline is further studied. The HHT showcases various evolving dominant frequency components over time in contrast to the PSD representation. The frequency spectrum from the HHT displays less variation over time at each sensor position compared to the predominant peak value derived from the PSD of the acceleration signal. The spatial variability in the predominant frequency obtained from HHT is also less than the PSD. Therefore, the ability of HHT to discriminate damages needs further investigations.

Investigating three-dimensional vortex evolution in centrifugal pump under rotating stall conditions using tomographic particle image velocimetry

A rotating stall in centrifugal pumps commonly occurs under off-design operations, which is a detrimental phenomenon leading to flow instabilities, pressure fluctuations, and reduced performance. A time-resolved non-intrusive three-dimensional (3D) flow visualization method is developed for investigating complex vortex structures in centrifugal pumps based on Omega vortex identification and tomographic particle image velocimetry (tomo-PIV). A special-made centrifugal pump prototype was developed with acrylic glass allowing for optical access. This method enables both qualitative and quantitative analysis of high spatiotemporal resolution on flow behaviors and dynamics under various stall conditions. The ultra-high sampling frequency realized over 40 time-consecutive observations per revolution under 0.2 Qd, 0.4 Qd, 0.6 Qd, and 0.8 Qd. It captures the instantaneous evolution of vortex structures that undergoes a growth–breakup transition within 7–9 ms. The rotating stall mechanism is revealed experimentally from the evolution of the vortex structure. Our analysis shows the tomo-PIV's additional velocity component aids in understanding the 3D characteristics of the stall. A substantial region of reverse flow in the z-axis direction is observed under 0.2 Qd. Vortex structures are more prone to blockage at the impeller inlet, exacerbating the stall phenomenon. As the flow rate increases, the velocity distributions across different layers exhibit a laminar characteristic with a more uniform profile. The vortex structures extend radially and migrate toward the outlet. The evolutions of the stall vortex, wake vortex, and inlet vortex share the same dominant frequency components (4.75fn and 5.25fn), but the flow rate affects the proportion of different frequency components.

Electric Motor by using Machine Learning.

This research paper addresses the crucial issue of early bearing failure within electric motors, a common problem impacting industrial operations. It begins with a thorough problem identification and historical study of asset failures to pinpoint strategic sensor placement on the motor casing, specifically targeting the bearing area. Time series data acquisition captures the dynamic motor behavior, with LabVIEW software facilitating visualization through frequency plots and spectral graphs. Subsequent preprocessing involves modifying data structure and computing vibration metrics like RRMS and IRRMS to quantify vibration levels. The processing stage consists of condition monitoring using structured supervised datasets transformed into the frequency domain in MATLAB. Interpretation of results enables bearing health assessment and identification of dominant frequency components. Additionally, the remaining useful life of the bearing is estimated using the Support Vector Classifier algorithm, supplemented by RRMS and IRRMS to enhance prediction accuracy. A machine learning model, developed in Python and trained on bearing datasets, predicts the remaining useful life based on sensor-collected data. This comprehensive methodology aims to provide insights into bearing health, facilitating proactive maintenance and optimizing industrial operations

An Efficient Narrowband FxLMS Algorithm for A 3-Axis Active Mount

The Active Mount (AM) controls the vibrations caused by the movement of a mechanical system resting on top of it. Vibration caused by mechanical systems often consists of several dominant frequency components. Therefore, we need an adaptive algorithm controlling tonal vibration components effectively. The FxLMS algorithm widely used for active vibration control raises several issues as the tone number and control channel increase. This paper proposes an efficient narrowband FxLMS algorithm for controlling multiple tonal vibrations occurring in a 3-axis AM. To this end, the secondary path's magnitude and phase responses are modeled tone-by-tone, independently using two-tap filters. Narrowband control filters are constructed for each tone using a narrowband signal model. In addition, a reference sensor is mounted on top of AM, from which tonal references are estimated using adaptive notch filters (ANFs). The proposed algorithm was simulated using transfer functions obtained in a 3-axis AM. Results show that the proposed algorithm effectively controls multiple tonal components by minimizing total errors measured along the 3-axis.

Signal Spectrum Analysis of Sediment Water Impact of Hydraulic Turbine Based on ICEEMDAN-Wavelet Threshold Denoising Strategy

Studies show that sediment erosion is one of the main factors attributing to hydraulic turbine failure. The present paper represents an investigation into acoustic vibration signals generated by the water flow impacting the hydraulic turbine runner under three different operating conditions. Collected signals were denoised using the ICEEMDAN-wavelet threshold method, and then the spectral characteristics and sample entropy characteristics of the signals for the three operating conditions were analyzed. The results show that when clean water flows through the hydraulic turbine, the sample entropy reaches its smallest values and the dominant frequency component in the spectrogram is 59.39 Hz. When transitioning from clean water to the flood flow containing 2–4 mm sediment particles, the sample entropy is increasing and a high-frequency component higher than 59.39 Hz becomes the prominent frequency of the spectrogram. Meanwhile, the formation of high-frequency components increases with the sand-containing particle size. Based on the spectral characteristics and sample entropy characteristics of the acoustic vibration signals under different operating conditions, it can provide a reference for the sand avoidance operation of the hydraulic turbine during flood season. In addition, it provides a supplement to the existing hydraulic turbine condition’s monitoring systems and a new avenue for subsequent research on early warning of hydraulic turbine failure.

Research on the Hydraulic Excitation Characteristics of the Top Cover Caused by the Radial Installation Deviation of the Seal of a 1GW Francis Turbine

The radial installation deviation of the turbine runner will change the gap flow between the upper crown and the lower ring seal, which will affect the radial force of the runner and the hydraulic excitation characteristics of the top cover. This research focuses on the 1GW Francis turbine on the right bank of the Baihetan hydropower station. The pressure distribution along the circumference of the top cover was analyzed, and the effects of deviations on the specific generation of hydraulic excitation forces were studied. This research shows that the increase in radial deviation will slightly reduce the output and efficiency, and the radial force on the runner increases parabolically. When the radial deviation is 1.5 mm, the radial force is 5.9 times higher compared to the case without any deviation, and the radius of the fitting circle of the radial force behavior trajectory increases with the increase in radial deviation. In addition, the radial deviation has little effect on the internal flow of the runner and the pressure distribution in the upper crown chamber. The dominant frequency components at the upstream monitoring points include fn, 15 fn, 24 fn, and 30 fn. The dominant frequency components at the downstream monitoring points include the blade passing frequencies of 15 fn and 30 fn. However, with the increase in radial deviation, the fluctuation amplitudes exhibit an asymmetric distribution, the uniformity of the pressure distribution in the circumferential direction of the labyrinth seal area becomes significantly worse, and the waveform of the downstream monitoring points changes significantly and presents a non-uniform distribution in one rotation cycle.

Output current harmonic analysis and suppression method for PMSM drive system with modular multilevel converter

AbstractAiming at the output current distortion problem in the low frequency operation of permanent magnet synchronous motor (PMSM) drive system of modular multilevel converter (MMC), the essential relationship between the output current distortion and the injected components is analyzed in this paper. A current harmonic suppression method combining feedforward and feedback compensation is proposed. Firstly, the feedback control method is applied to transform the MMC three‐phase output current into the dq rotating coordinate system at fundamental frequency ω. The harmonics of the AC terms to be suppressed are filtered out. The dominant fundamental frequency component turned into the DC term is retained. Then combined with feedforward control, the control performance of the system is improved. Secondly, the deadbeat predictive current control is improved by introducing an integral link in it. System disturbances caused by parameter changes can be compensated in real time. The design of the control system is simplified and the static error is reduced. Meanwhile, the system has the advantages of small torque pulsation and fast speed response. Finally, the PMSM drive system model of the 11‐level MMC is built in MATLAB/Simulink. The correctness and effectiveness of the proposed suppression method are verified.

Contactless Method for Measurement of Surface Roughness Based on a Chromatic Confocal Sensor

The methodology for assigning and assessing the surface quality is used at various stages of the product life cycle: during the design and technological preparation of production, the production itself, and during the control (testing) of products. The development of advanced technologies requires in situ part control. A non-contact in situ surface roughness measuring system is proposed in this paper. The proposed system utilizes a chromatic confocal sensor, and profile data, waviness data, roughness data, Ra, and Rz parameters are generated in the developed data-processing software. The assembled measuring system based on the chromatic confocal laser sensor showed its performance in assessing the roughness parameter Ra, from 0.34 µm to more than 12 µm, which covers a common range of milling, turning, and grinding. In this range, measurement relative errors can be controlled within 10%. Frequency analysis and correlation analysis of profilograms were performed. Frequency analysis made it possible to establish the dominant frequency components that occur in the profilogram of the samples, while correlation analysis was used to develop a methodology for identifying the deterministic and random components of the processed surface profile signal. The results of the analysis can be further used to develop diagnostic functions for process monitoring based on profilogram estimates, such as the autocorrelation function and the power spectrum density.

Characterizing onboard noise in ships: Insights from statistical, machine learning and advanced noise index analyses

Understanding the specific characteristics of onboard noise can significantly benefit human activity in different kinds of ships. Here, the perceived noise can significantly change from space to space in terms of both levels and frequency content, depending on location onboard in respect to the primary sources and on propagation paths. Moreover, secondary sources as ventilation systems contribute to generate a complex soundscape for each ship. In this work, noise spectra reported in the literature were collected and analyzed to obtain an overview of the noise characteristics in different spaces onboard military and commercial vessels, i.e. accommodation, navigation bridge, and other workspaces. A prevalence of high noise levels in the different spaces that can be over the commonly accepted limits was found, characterized by higher levels at low and high frequencies. Different noise indexes not yet adopted in this sector were tested on the data to derive more information about onboard noise annoyance. Supervised and unsupervised machine learning algorithms were also applied to qualitatively classify and study the collected spectra. The analysis shows the presence of different spectral typologies, i.e. balanced spectral shapes (quasi-linear decay of the noise level per octave band with no dominant frequency components) or unbalanced ones.

Exploring nonintrusive measurements of spatio‐temporal portrait of microservices

AbstractAs cloud native technology advances, the scale and complexity of applications built on microservice architecture continue to expand, leading to increasingly intricate differences between software within the same application. Microservice applications, offering high flexibility, are deployed in data centers as black boxes from the users' perspective, leaving them with no insight into the orchestration of cloud service providers. Consequently, users face challenges in promptly recognizing performance imbalances within their deployed applications. Meanwhile, cloud service providers may cut costs by offering a mix of qualified and unqualified services, potentially deceiving users. To enhance the understanding of microservice application organization, we propose a non‐intrusive measurement framework, termed NMPI. NMPI facilitates rapid identification of microservice application defects, offering insights into cloud services and detecting fraudulent behavior in microservice‐based applications. We model microservice applications using a queue analysis‐based approach and filter the dominant frequency components of average response time signals by employing k‐means on the fast fourier transform (FFT). Our model constructs a library of performance portraits for various software, with these portraits resembling human fingerprints that carry and mark the software's internal information. Utilizing a two‐tier microservices‐based application incorporating a database as a case study allows us to demonstrate the effectiveness of NMPI. Our experimental results show that NMPI can produce differentiable profiles of data service performance portraits across a diverse and extensive range of workloads, enabling the identification of software types and the analysis of performance conditions.

Maximum power point tracking control of wind turbines based on equivalent sinusoidal wind

The optimal parameter model method for maximum power point tracking (MPPT) control of wind turbines has good wind energy capture performance. However, there is a precondition that the quantitative relationship between optimal control parameters and turbulence characteristics is accurately described. The resolution of the existing turbulence frequency characterization indicators are insufficient. Therefore, a large number of sample data need to be obtained to clearly show the statistical relationship between them and the optimal control parameters, which is extremely time-consuming, and has a negative impact on the optimization results of control parameters. This paper equates the turbulent wind to the sinusoidal wind with the objective of the results of both acting on wind turbines are the same, so as to realize the extraction and index description of the dominant turbulent frequency components strongly related to the optimal control parameters. On this basis, a MPPT control method based on equivalent sinusoidal wind is proposed. This method greatly reduces the required sample size, and makes the setting of control parameters closer to the optimal value, which can further improve the efficiency of wind energy capture. Simulation analysis and experimental results based on wind turbine simulator verify the effectiveness of the proposed method.

Noise in ICUs: Review and Detailed Analysis of Long-Term SPL Monitoring in ICUs in Northern Spain.

Intensive care units (ICUs) are busy and noisy areas where patients and professional staff can be exposed to acoustic noise for long periods of time. In many cases, noise levels significantly exceed the levels recommended by the official health organisations. This situation can affect not only patient recovery but also professional staff, making ICUs unhealthy work and treatment environments. To introduce the measures and reduce the acoustic noise in the ICU, acoustic noise levels should first be measured and then appropriately analysed. However, in most studies dealing with this problem, measurements have been performed manually over short periods, leading to limited data being collected. They are usually followed by insufficient analysis, which in turn results in inadequate measures and noise reduction. This paper reviews recent works dealing with the problem of excessively high noise levels in ICUs and proposes a more thorough analysis of measured data both in the time and frequency domains. Applied frequency domain analysis identifies the cyclic behaviour of the measured sound pressure levels (SPLs) and detects the dominant frequency components in the SPL time series. Moreover, statistical analyses are produced to depict the patterns and SPLs to which patients in ICUs are typically exposed during their stay in the ICU. It has been shown that the acoustic environment is very similar every night, while it can vary significantly during the day or evening periods. However, during most of the observed time, recorded SPLs were significantly above the prescribed values, indicating an urgent need for their control and reduction. To effectively tackle this problem, more detailed information about the nature of noise during each of the analysed periods of the day is needed. This issue will be addressed in the continuation of this project.

Design of a non-invasive sensing system for diagnosing gastric disorders

Gastric disorders are widely spread among the population of any age. At the moment, the diagnosis is made by using invasive systems that cause several side effects. The present manuscript proposes an innovative non-invasive sensing system for diagnosing gastric dysfunctions. The Electro-GastroGraphy (EGG) technique is used to record myoelectrical signals of stomach activities. Although EGG technique is well known for a long time, several issues concerning the signal processing and the definition of suitable diagnostic criteria are still unresolved. So, EGG is to this day a trial practice. The authors want to overcome the current limitations of the technique and improve its relevance. To this purpose, a smart EGG sensing system has been designed to non-invasively diagnose gastric disorders. In detail, the system records the gastric slow waves by means of skin surface electrodes placed in the epigastric area. Cutaneous myoelectrical signals are so acquired from the body surface in proximity of stomach. Electro-gastrographic record is then processed. According to the diagnostic model designed from the authors, the system estimates specific diagnostic parameters in time and frequency domains. It uses Discrete Wavelet Transform to obtain power spectral density diagrams. The frequency and power of the EGG waveform and the dominant frequency components are so analyzed. The defined diagnostic parameters are put in comparison with the reference values of a normal EGG in order to estimate the presence of gastric pathologies by the analysis of arrhythmias (<em>tachygastria</em>, <em>bradygastria</em> and irregular rhythm). The paper aims to describe the design of the system and of the arrhythmias detection algorithm. Prototype development and experimental data will be presented in future works. Preliminary results show an interesting relevance of the suggested technique so that it can be considered as a promising non-invasive tool for diagnosing gastrointestinal motility disorders.

Signature barcodes for online verification

As a sub-branch of behavioral biometrics, online signature verification systems deal with unique signing characteristics, which could be better differentiated by extraction of habitual singing styles instead of geometric features in case of perfect forgery. Even if the signatures are geometrically identical, speed and frequency components of the signing process might significantly vary. Therefore, a novel framework is introduced as a new signature verification protocol for touchscreen devices using barcodes containing the dominant frequency component of the speed signals. A special interface is designed as signature tracker to extract the displacement data sampled from the signing process. The speed signals are interpolated from the displacement data and the frequency components of the signals are computed by scalograms analysis governed by continuous wavelet transformations (CWT). The signature barcodes are generated as 4-scale scalograms and classified by support vector machines (SVM). Among several compatible wavelets, Gaussian derivative wavelet is selected for generating scalograms and the results of the process are calculated as 2.25% FAR, 2.75% FRR and 2.81%EER for our dataset. The framework is also tested with SVC2004 data that we achieved 0% FAR, 9.33% FRR and 8%EER, also with SUSIG-Visual, SUSIG-Blind, MOBISIG databases and we reached between 1.22%-3.62% average EERs, which are competitive among the relevant results. Given the promising outcomes, the signature barcoding is very reliable method which could be executed by a simple touchscreen interface collecting the barcodes for storing and benchmarking when needed.

Predicting the azimuth of natural fractures and in situ horizontal stress: A case study from the Sichuan Basin, China

The azimuth of fractures and in situ horizontal stress are important factors in planning horizontal wells and hydraulic fracturing for unconventional resource plays. The azimuth of natural fractures can be directly obtained by analyzing image logs. The azimuth of the maximum horizontal stress [Formula: see text] can be predicted by analyzing the induced fractures on image logs. The clustering of microseismic events also can be used to predict the azimuth of in situ maximum horizontal stress. However, the azimuth of natural fractures and the in situ maximum horizontal stress obtained from image logs and microseismic events are limited to the wellbore locations. Wide-azimuth seismic data provide an alternative way to predict the azimuth of natural fractures and maximum in situ horizontal stress if the seismic attributes are properly calibrated with interpretations from well logs and microseismic data. To predict the azimuth of natural fractures and in situ maximum horizontal stress, we have focused our analysis on correlating the seismic attributes computed from pre- and poststack seismic data with the interpreted azimuth obtained from image logs and microseismic data. The application indicates that the strike of the most-positive principal curvature [Formula: see text] can be used as an indicator for the azimuth of natural fractures within our study area. The azimuthal anisotropy of the dominant frequency component of offset vector title seismic data can be used to predict the azimuth of maximum in situ horizontal stress within our study area that is located in the southern region of the Sichuan Basin, China. The predicted azimuths provide important information for the subsequent well planning and hydraulic fracturing.

Dominant frequency component tracking of noisy time‐varying signals using the linear predictive coding pole processing method

The linear predictive coding pole processing (LPCPP) method proposed in our previous work overcomes the shortcomings of the LPC method, especially its sensitivity to noise and the filter order. The LPCPP method is a parameterised method that involves processing the LPC poles to produce a series of reduced-order filter transfer functions to estimate the dominant frequency components of a signal. This paper analyses the ability of the LPCPP method to track the frequency changes of noisy, time-varying signals in real-time. Linear chirped frequency modulation signals are used in a series of experiments to simulate signals with different rates of frequency change. The results show that the LPCPP method can achieve real-time tracking of the dominant frequency in the signal and outperforms the LPC method under different frequency change rates and different noise levels. Specifically, the valid estimate percentage of LPCPP is up to 41.3% higher than that of LPC which indicates that the LPCPP method significantly improves the validity of frequency estimates.

Evaluating the thickness of thin-bed seams using seismic data on the example of the Tula-Bobrikovian sediments in the Republic of Tatarstan

Background.Determining the productive deposit thickness is of fundamental importance for assessing the reserves of oil and gas fields. 3D seismic data is used to assess the thickness of seams in the interwell space. However, owing to the limited vertical resolution of seismic data, estimating thicknesses of thin deposits (less than 20 m) is challenging.Aim.To evaluate different approaches to calculating the thickness of the productive deposits based on seismic data with the purpose of selecting the most optimal approach.Materials and methods.We compared the results obtained using different approaches to assessing the productive deposit thickness of the Tula-Bobrikovian age in the interwell space, including the convergence method (calculating the thickness for oilwells with no seismic data used), the use of seismic attributes to calculate the “seismic attribute — reservoir thickness” dependency (for attributes, dominant frequency and mono-frequency component at 60 Hz), estimation of the thickness from the seismic signal shape. Cokriging was used to calculate inferred power maps from seismic attribute data and to classify them by waveform. For each of the techniques, the crossvalidation method and calculating the root-mean-square deviation were used as quality criteria.Results.When assessing the accuracy of thickness map development, the root-mean-square deviation was 12.3 m according to convergence method, 10.2 m — to the dominant frequency attribute, 7.2 m — to the attribute of the monofrequency component at 60 Hz and 6.3 m — to the signal shape classification. The latter method yielded the best results, and the developed thickness map allowed paleo-cut to be traced.Conclusions.Applying the thickness estimation method based on the seismic signal shape allows the value of the root-mean-square deviation to be reduced by a factor of 2 compared to that of the widely adopted convergence method. This approach permits productive deposits thickness to be more accurately estimated and hydrocarbon reserves to be determined.

Modulation Model of the Photoplethysmography Signal for Vital Sign Extraction.

This paper introduces an amplitude and frequency modulation (AM-FM) model to characterize the photoplethysmography (PPG) signal. The model indicates that the PPG signal spectrum contains one dominant frequency component - the heart rate (HR), which is guarded by two weaker frequency components on both sides; the distance from the dominant component to the guard components represents the respiratory rate (RR). Based on this model, an efficient algorithm is proposed to estimate both HR and RR by searching for the dominant frequency component and two guard components. The proposed method is performed in the frequency domain to estimate RR, which is more robust to additive noise than the prior art based on temporal features. Experiments were conducted on two types of PPG signals collected with a contact sensor (an oximeter) and a contactless visible imaging sensor (a color camera), respectively. The PPG signal from the contactless sensor is much noisier than the signal from the contact sensor. The experimental results demonstrate the effectiveness of the proposed algorithm, including under relatively noisy scenarios.

The influence of frequency component content on the selection result of hearing protectors

Hearing protectors are selected for workstation noise using the octave band (OB), HML and SNR methods. The purpose of this study was to determine how the frequency components of the noise can affect the selection of hearing protectors. In total, 55 hearing protectors were selected for four types of real occurring noise, high-frequency noise, low-frequency noise and eight simulated noises. Analysis showed that the noise type affects the accuracy of selection carried out using the HML and SNR methods. For a noise with dominant frequency components, the result for selection carried out using the HML method deviates from the result using the OB method by 7 and 9 dB for earmuffs and earplugs, respectively. The study shows that use of the HML and SNR methods may lead to wrong assessment of the effectiveness of hearing protection with the selected hearing protectors.