Accuracy evaluation of quantitative diagnosis method of liver fibrosis based on multi-Rayleigh model using optimal combination of input moments

2020 ◽  
Vol 59 (SK) ◽  
pp. SKKE27
Author(s):  
Chuang Zhang ◽  
Shinnosuke Hirata ◽  
Hiroyuki Hachiya
2009 ◽  
Vol 09 (04) ◽  
pp. 579-588 ◽  
Author(s):  
TADASHI YAMAGUCHI ◽  
HIROYUKI HACHIYA

At present, percutaneous liver biopsy is the gold standard in assessing liver fibrosis such as hepatitis and cirrhosis, but there could be sampling error, and specimens might not represent the state of the whole liver accurately because only about 0.002% of the organ is sampled. In this research, we propose the three-dimensional fiber structure extraction echo filter to realize a quantitative ultrasonic diagnosis. The filter is designed based on a statistical theory, and it is possible to reduce the noise contained in a back scattered ultrasonic echo signal, and to visualize the structure of a fiber.


Measurement ◽  
2020 ◽  
Vol 152 ◽  
pp. 107271 ◽  
Author(s):  
Shuanfeng Zhao ◽  
Shijun Li ◽  
Wei Guo ◽  
Chuanwei Zhang ◽  
Bowen Cong

2020 ◽  
Vol 10 (6) ◽  
pp. 1946 ◽  
Author(s):  
Liubang Han ◽  
Kuosheng Jiang ◽  
Qidong Wang ◽  
Xuanyao Wang ◽  
Yuanyuan Zhou

High impact and strong noise complicate the response of reciprocating compressor (RC). It requires a complex signal processing method that is a single response-based or excitation-based fault diagnosis method applied to RC valve leakage fault diagnosis. This paper proposes a quantitative diagnosis method of RC valve leakage that is based on system characteristic diagnosis method. First, the current signal of the RC induction motor and the cylinder vibration signal are introduced as the excitation and response signals, the mathematical model of the RC motor current is established, and the influence mechanism of the valve leakage on the RC vibration is analyzed. Subsequently, the ensemble empirical mode decomposition and comb filter are respectively used to extract the fault characteristic information of excitation signal and response signal to obtain the excitation condition indicators (CIs), response CIs, and system CIs. Finally, the support vector machine based on the obtained CIs classified the valve leakage failure patterns of different severity, and a fault diagnoser was constructed for the quantitative diagnosis of valve leakage fault. The results of experiment and application proved that the proposed method could realize the quantitative diagnosis of RC valve leakage fault while using simple signal processing technology.


Sign in / Sign up

Export Citation Format

Share Document