Search

In this paper an ion current sensing method is proposed for early detection of lean blowout (LBO) in a pulse combustor. The method was also compared with the pressure measurement method. Near LBO characteristics in the pulse combustor were inferred from ion current and pressure time domain signals recorded for different operating conditions and initially analyzed using the spectral analysis method. As LBO approached, low frequency (below 20 Hz) components were gradually observed and the spectral energy between 0 and 20 Hz gradually increased while the energy of the working frequency components decreased. The ratio of the working frequency components energy fraction over the low frequency components energy fraction could thus be treated as an indicator of LBO, denoted by stability to instability ratio ( SIR ). The index SIR remained at a high level under stable conditions and decreased steeply when LBO was approached. Based on the ion current sensing method and the prediction index SIR , the gas supply pressure for stable operation in the pulse combustor was suggested to be more than 15.2 kPa. The result shows that the proposed ion current sensing method and the index SIR can help to estimate the LBO limit of the pulse combustor and avert the unexpected LBO. Compared to pressure sensing, ion current sensing was observed to be more sensitive to LBO events, leaving sufficient time for combustion control. [ABSTRACT FROM AUTHOR]

This paper presents a set of novel features of heart sound for the detection of the abnormality of heart sounds and classification of heart murmurs. The features include energy fraction of the first and the second heart sounds ( S 1– S 2 EF ), energy fraction of heart murmur ( HMEF ), the maximum energy fraction of heart sound frequency sub-band ( HSEFmax ), sample entropy of the first and the second heart sounds component ( S 1– S 2 sampen ) and sample entropy of heart murmur component ( HMsampen ). Firstly, the heart sound signals were de-noised and normalized, then decomposed by wavelet packet. The features, such as energy fraction and sample entropy were calculated from the reconstructed selective frequency components of heart sound signals. The support vector machine (SVM) was employed as a classifier to detect the abnormality of heart sound and discriminate heart murmurs. A dataset consisting of 80 normal heart sounds and 167 systolic heart murmurs samples, segmented from 40 healthy volunteers and 67 patients, were used to test and validate the proposed method. The performance of our proposed method was assessed in terms of sensitivity, specificity and accuracy. The result showed that our proposed method exhibited a satisfactory performance with a high accuracy of 97.17%, a specificity of over 98.55% and a sensitivity of over 93.48%. This suggests that the presented method can be used as an effective assistance for cardiac auscultation. [ABSTRACT FROM AUTHOR]