Study of Autoregressive (AR) Spectrum Estimation Algorithm for Vibration Signals of Industrial Steam Turbines

Huang, Junyou

doi:10.14257/ijca.2014.7.8.32

Cited by 14 publications

(5 citation statements)

References 16 publications

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“…Additionally, considering that the sampling frequency of the FHR signals we studied was the only 4 Hz, the AR modeling was appropriate for the IMF, spectral-based analysis. The AR modeling method effectively describes the peaks of a narrow-band power spectrum [52], and it requires only a fraction of the signal samples that are needed by standard methods, such as the FFT, in order to obtain the same spectral resolution.…”

Section: Methodsmentioning

confidence: 99%

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

2019

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Fetal monitoring is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction signals obtained with a cardiotocograph (CTG). Unfortunately, CTG analysis is difficult, and the interpretation problems are mainly associated with the analysis of FHR decelerations. From that perspective, several approaches have been proposed to improve its analysis; however, the results obtained are not satisfactory enough for their implementation in clinical practice. Current clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms. In previous works, we have shown that the complete ensemble empirical mode decomposition with adaptive noise, in combination with time-varying autoregressive modeling, may be useful for the analysis of those characteristics. In this work, based on this methodology, we propose to analyze the FHR deceleration episodes separately. The main hypothesis is that the proposed feature extraction strategy applied separately to the complete signal, deceleration episodes, and resting periods (between contractions), improves the CTG classification performance compared with the analysis of only the complete signal. Results reveal that by considering the complete signal, the classification performance achieved 81.7% quality. Then, including information extracted from resting periods, it improved to 83.2%.

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Section: Methodsmentioning

confidence: 99%

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

2019

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“…The classical stationary AR modeling [51] can adequately describe the peaks of a narrow-band power spectrum [55] and requires only a fraction of the signal samples that are required by standard methods, such as the FFT, in order to obtain the same spectral resolution. It is a technique for time series analysis in which a mathematical model is fitted to a sampled signal.…”

Section: Methodsmentioning

confidence: 99%

Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment

2019

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Cardiotocograph (CTG) is a widely used tool for fetal surveillance during labor, which provides the joint recording of fetal heart rate (FHR) and uterine contraction data. Unfortunately, the CTG interpretation is difficult because it involves a visual analysis of highly complex signals. Recent clinical research indicates that a correct CTG assessment requires a good understanding of the fetal compensatory mechanisms modulated by the autonomic nervous system. Certainly, this modulation reflects variations in the FHR, whose characteristics can involve significant information about the fetal condition. The main contribution of this work is to investigate these characteristics by a new approach combining two signal processing methods: the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and time-varying autoregressive (TV-AR) modeling. The idea is to study the CEEMDAN intrinsic mode functions (IMFs) in both the time-domain and the spectral-domain in order to extract information that can help to assess the fetal condition. For this purpose, first, the FHR signal is decomposed, and then for each IMF, the TV-AR spectrum is computed in order to study their spectral dynamics over time. In this paper, we first explain the foundations of our proposed features. Then, we evaluate their performance in CTG classification by using three machine learning classifiers. The proposed approach has been evaluated on real CTG data extracted from the CTU-UHB database. Results show that by using only conventional FHR features, the classification performance achieved 78, 0%. Then, by including the proposed CEEMDAN spectral-based features, it increased to 81, 7%. INDEX TERMS Biomedical signal processing, cardiotocograph, empirical mode decomposition, fetal heart rate, spectral analysis, time-varying autoregressive modeling.

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“…Moreover at that time, the estimation approach of spectrum by AR estimation and conventional estimation for turbine vibration signals in case of industrial environment has been compared successfully. Y-W method has been used to estimate the spectrum of the signals generated by the vibration of turbine [10]. In addition in 2014 ranging system has been studied for pseudorandom continuous wave signal [11].…”

Section: Litarature Reviewmentioning

confidence: 99%

Estimation of Optimum Number of Poles for Random Signal by Yule-Walker Method

et al. 2017

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Study of Autoregressive (AR) Spectrum Estimation Algorithm for Vibration Signals of Industrial Steam Turbines

Abstract: Abstract

Cited by 14 publications

References 16 publications

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

Independent Analysis of Decelerations and Resting Periods through CEEMDAN and Spectral-Based Feature Extraction Improves Cardiotocographic Assessment

Cardiotocographic Signal Feature Extraction Through CEEMDAN and Time-Varying Autoregressive Spectral-Based Analysis for Fetal Welfare Assessment

Estimation of Optimum Number of Poles for Random Signal by Yule-Walker Method

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