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Nowadays, acoustic and vibration signals have been widely used to detect crack and its degree on compressor blades for non-destructive evaluation and structural health monitoring. Due to complex working conditions and interference of strong noise, single signal-based methods reach unsatisfactory accuracy. To improve the reliability and accuracy of crack detection, the two-level fusion method based on Mahalanobis distance (MD) and self-attention mechanism is proposed based on multi-acoustic, vibration, and acoustic emission information. First, the MD is calculated to measure the similarity between raw samples and group, which can fuse the same type of samples in data level. Then, the processed raw and data-level fusion samples are inputted to one-dimensional convolutional neural network, and the preliminary decisions are obtained. Finally, the decision-level fusion based on self-attention mechanism and credit assignment is proposed to modify and correct the preliminary decisions. The compressor experiments are implemented to test the proposed method under single and mixed working conditions with strong noise intensity. Further, the results illustrate that the proposed method can accurately detect crack for compressor blades. By comparing with other data fusion approaches, the advantage of the proposed method is validated under complex conditions. [ABSTRACT FROM AUTHOR]

For centrifugal fans, the emergence of amplitude modulation (AM) components is a pivotal feature for blade crack fault. However, AM features extraction are challenging under strong interferences. A novel method is proposed to solve this problem using variational mode decomposition (VMD) and time-delayed feedback nonlinear tri-stable stochastic resonance (TFNTSR). Firstly, an improved degree of cyclostationarity (IDCS) index is constructed for parameters optimization. Then, intrinsic mode functions (IMFs) are obtained using the VMD method, and one of them which has the maximum IDCS value is selected. Finally, the selected IMF is further processed by the TFNTSR method, thereby completing the blade crack detection. Verified by simulations and experiments, VMD-TFNTSR method has a better performance on blade crack detection. [ABSTRACT FROM AUTHOR]

To explore the perception of selective laser trabeculoplasty (SLT) among physicians and patients and the acceptance of using SLT as first-line treatment for glaucoma. Exploratory descriptive qualitative study based on semi-structured interviews with glaucoma specialists and patients in two eye hospital of Zhejiang and Guangdong Provinces, China. Data were analyzed using the thematic analysis framework approach. 11 glaucoma specialists and 12 glaucoma patients were interviewed. Four themes were identified in the data: Physicians' preferences for treatment types, patient views on treatment types, decision-making during the physician-patient encounter and feasibility of SLT as a first-line therapy. Advantages of SLT include safety and repeatability with limited side effects. However, concerns about the durability of the effect of the treatment were often mentioned by both physicians and patients. Some factors such as practice preference, personal motivation and patient characteristics may influence treatment choice. Most patients lack knowledge about SLT and hold high expectations of their treatments. Physicians report insufficient evidence supporting the use of SLT as first line therapy. Physicians report the need for locally relevant, evidence-based guidelines regarding the use of SLT in the treatment of glaucoma. SLT was suggested as the first-line treatment of glaucoma due to its reliable efficacy and potential advantage. Results from our study offer important insight into barriers of increasing the uptake of SLT, which also provides some guidance for the use of SLT in the future. [ABSTRACT FROM AUTHOR]

Objectives: To assess the feasibility and performance of Turbulence (Tur) index as a quantitative tool for carotid artery flow turbulence; to detect and compare the blood flow patterns of common carotid artery (CCA) and carotid bulb (CB) at different ages and cardiac phases in healthy adults, and thus interpret the evolvement of etiology difference between CCA and CB. Methods: Carotid flow characteristics of 40 healthy volunteers were evaluated quantitatively by a high‐frame rate vector flow imaging. Three types of flow patterns were defined depending on the distributive range of complex flow during systole in CB. Comparison of mean Tur value in CCA and CB at different age groups and cardiac phases was performed. And the correlation between Tur value and the diameter ratio of proximal internal carotid artery to common carotid artery (DRpro‐ica/cca) was tested. Results: Mean Tur values in CB were remarkably higher than that in CCA, whether during systole or diastole (P <.001). Meanwhile Tur values in CB during systole were significantly higher than that during diastole (P <.001). Flow complexity of CB showed variations among 40 participants especially in systole, whereas the flow pattern of CCA was relatively consistent. Mean Tur values were positively correlated with DRpro‐ica/cca in CB (ρ = 0.69, P <.05). Conclusions: V Flow imaging provided a reliable method—Tur, for quantitative analysis of carotid blood flow. It had potential to be further applied in distinguishing complex hemodynamic characteristics in high‐risk people of carotid diseases for the risk stratification of cardiovascular events. [ABSTRACT FROM AUTHOR]