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    Wyświetlanie 1-4 z 4
    Tytuł:
    Detection and diagnosis of bearing defects using vibration signal processing
    Autorzy:
    Bouaouiche, Karim
    Menasria, Yamina
    Khalfa, Dalila
    Powiązania:
    https://bibliotekanauki.pl/articles/27309892.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    vibration signal
    bearing
    signal processing
    envelope spectrum
    fault frequency
    sygnał wibracyjny
    łożysko
    przetwarzanie sygnałów
    częstotliwość usterek
    Opis:
    This work presents an analysis of vibration signals for bearing defects using a proposed approach that includes several methods of signal processing. The goal of the approach is to efficiently divide the signal into two distinct components: a meticulously organized segment that contains relatively straightforward information, and an inherently disorganized segment that contains a wealth of intricately complex data. The separation of the two component is achieved by utilizing the weighted entropy index (WEI) and the SVMD algorithm. Information about the defects was extracted from the envelope spectrum of the ordered and disordered parts of the vibration signal. Upon applying the proposed approach to the bearing fault signals available in the Paderborn university database, a high amplitude peak can be observed in the outer ring fault frequency (45.9 Hz). Likewise, for the signals available in XJTU-SY, a peak is observed at the fault frequency (108.6 Hz).
    Źródło:
    Archive of Mechanical Engineering; 2023, LXX, 3; 433--452
    0004-0738
    Pojawia się w:
    Archive of Mechanical Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Single-channel EEG processing for sleep apnea detection and differentiation
    Autorzy:
    Prucnal, Monika A.
    Polak, Adam G.
    Powiązania:
    https://bibliotekanauki.pl/articles/27311744.pdf
    Data publikacji:
    2023
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    single-channel EEG
    sleep apnea detection
    optimization of signal processing
    medical decision support
    Opis:
    Sleep apnea syndrome is a common sleep disorder. Detection of apnea and differentiation of its type: obstructive (OSA), central (CSA) or mixed is important in the context of treatment methods, however, it typically requires a great deal of technical and human resources. The aim of this research was to propose a quasi-optimal procedure for processing single-channel electroencephalograms (EEG) from overnight recordings, maximizing the accuracy of automatic apnea or hypopnea detection, as well as distinguishing between the OSA and CSA types. The proposed methodology consisted in processing the EEG signals divided into epochs, with the selection of the best methods at the stages of preprocessing, extraction and selection of features, and classification. Normal breathing was unmistakably distinguished from apnea by the k-nearest neighbors (kNN) and an artificial neural network (ANN), and with 99.98% accuracy by the support vector machine (SVM). The average accuracy of multinomial classification was: 82.29%, 83.26%, and 82.25% for the kNN, SVM and ANN, respectively. The sensitivity and precision of OSA and CSA detection ranged from 55 to 66%, and the misclassification cases concerned only the apnea type.
    Źródło:
    Metrology and Measurement Systems; 2023, 30, 2; 323--336
    0860-8229
    Pojawia się w:
    Metrology and Measurement Systems
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Comparison of Moving Average and Differential Operation for Wheeze Detection in Spectrograms
    Autorzy:
    Hsueh, Meng-Lun
    Chen, Jin-Peng
    Lu, Bing-Yuh
    Wu, Huey-Dong
    Liu, Pei-Yi
    Powiązania:
    https://bibliotekanauki.pl/articles/31340111.pdf
    Data publikacji:
    2022
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    differential operation
    moving average
    signal
    lung sound
    wheeze
    Opis:
    A moving average (MA) is a commonly used noise reduction method in signal processing. Several studies on wheeze auscultation have used MA analysis for preprocessing. The present study compared the performance of MA analysis with that of differential operation (DO) by observing the produced spectrograms. These signal preprocessing methods are not only applicable to wheeze signals but also to signals produced by systems such as machines, cars, and flows. Accordingly, this comparison is relevant in various fields. The results revealed that DO increased the signal power intensity of episodes in the spectrograms by more than 10 dB in terms of the signal-to-noise ratio (SNR). A mathematical analysis of relevant equations demonstrated that DO could identify high-frequency episodes in an input signal. Compared with a two-dimensional Laplacian operation, the DO method is easier to implement and could be used in other studies on acoustic signal processing. DO achieved high performance not only in denoising but also in enhancing wheeze signal features. The spectrograms revealed episodes at the fourth or even fifth harmonics; thus, DO can identify high-frequency episodes. In conclusion, MA reduces noise and DO enhances episodes in the high-frequency range; combining these methods enables efficient signal preprocessing for spectrograms.
    Źródło:
    Archives of Acoustics; 2022, 47, 3; 383-388
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Heart Rate Detection and Classification from Speech Spectral Features Using Machine Learning
    Autorzy:
    Usman, Mohammed
    Zubair, Mohammed
    Ahmad, Zeeshan
    Zaidi, Monji
    Ijyas, Thafasal
    Parayangat, Muneer
    Wajid, Mohd
    Shiblee, Mohammad
    Ali, Syed Jaffar
    Powiązania:
    https://bibliotekanauki.pl/articles/1953514.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czasopisma i Monografie PAN
    Tematy:
    heart rate from speech
    machine learning
    MFCC
    regression
    classification
    speech as a biomedical signal
    Opis:
    Measurement of vital signs of the human body such as heart rate, blood pressure, body temperature and respiratory rate is an important part of diagnosing medical conditions and these are usually measured using medical equipment. In this paper, we propose to estimate an important vital sign – heart rate from speech signals using machine learning algorithms. Existing literature, observation and experience suggest the existence of a correlation between speech characteristics and physiological, psychological as well as emotional conditions. In this work, we estimate the heart rate of individuals by applying machine learning based regression algorithms to Mel frequency cepstrum coefficients, which represent speech features in the spectral domain as well as the temporal variation of spectral features. The estimated heart rate is compared with actual measurement made using a conventional medical device at the time of recording speech. We obtain estimation accuracy close to 94% between the estimated and actual measured heart rate values. Binary classification of heart rate as ‘normal’ or ‘abnormal’ is also achieved with 100% accuracy. A comparison of machine learning algorithms in terms of heart rate estimation and classification accuracy is also presented. Heart rate measurement using speech has applications in remote monitoring of patients, professional athletes and can facilitate telemedicine.
    Źródło:
    Archives of Acoustics; 2021, 46, 1; 41-53
    0137-5075
    Pojawia się w:
    Archives of Acoustics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

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