isolation method
Recently Published Documents


TOTAL DOCUMENTS

633
(FIVE YEARS 135)

H-INDEX

46
(FIVE YEARS 4)

Author(s):  
Koichiro Tsutsumi ◽  
Eijiro Ueta ◽  
Hironari Kato ◽  
Kazuyuki Matsumoto ◽  
Shigeru Horiguchi ◽  
...  

Author(s):  
Farhad Forouharmajd ◽  
Shiva Soury ◽  
Mehran Mokhtari ◽  
Zahra Mohammadi

Background and purpose: Vibration caused by ventilation systems in buildings is one of the harmful physical factors that can cause harm to residents. Therefore, measuring and controlling vibration is important. Materials and Methods: In the first step of the study, the vibration accelerometer was placed on the base of a fan where the vibrations were sent toward the duct wall. A vibration assessment of the building was conducted in the other steps to compare with guidelines. In the next step, isolation method was used to control vibration. By placing the isolator on the duct wall, the accelerometer was located on the body of the duct wall and the value of vibration was measured in a millimeter per second. All stages of the experiment were performed in the Faculty of Health of Isfahan University of Medical Sciences in 2018. Results: The maximum vibration velocity reduction in the building was related to the frequency of 68 Hz, which reached 33 mm/s after isolation. In addition, the fan vibration at 485 Hz was equal to 2.1 m /s, which decreased to 2 mm /s after isolation. Conclusion: Comparison of vibration after fan isolation with standard showed that this method has been effective in reducing the fan vibration resulting in the vibration to reach below the standard.


Machines ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 337
Author(s):  
Amare Desalegn Fentaye ◽  
Valentina Zaccaria ◽  
Konstantinos Kyprianidis

The rapid advancement of machine-learning techniques has played a significant role in the evolution of engine health management technology. In the last decade, deep-learning methods have received a great deal of attention in many application domains, including object recognition and computer vision. Recently, there has been a rapid rise in the use of convolutional neural networks for rotating machinery diagnostics inspired by their powerful feature learning and classification capability. However, the application in the field of gas turbine diagnostics is still limited. This paper presents a gas turbine fault detection and isolation method using modular convolutional neural networks preceded by a physics-driven performance-trend-monitoring system. The trend-monitoring system was employed to capture performance changes due to degradation, establish a new baseline when it is needed, and generatefault signatures. The fault detection and isolation system was trained to step-by-step detect and classify gas path faults to the component level using fault signatures obtained from the physics part. The performance of the method proposed was evaluated based on different fault scenarios for a three-shaft turbofan engine, under significant measurement noise to ensure model robustness. Two comparative assessments were also carried out: with a single convolutional-neural-network-architecture-based fault classification method and with a deep long short-term memory-assisted fault detection and isolation method. The results obtained revealed the performance of the proposed method to detect and isolate multiple gas path faults with over 96% accuracy. Moreover, sharing diagnostic tasks with modular architectures is seen as relevant to significantly enhance diagnostic accuracy.


npj Vaccines ◽  
2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Heidi Peck ◽  
Karen L. Laurie ◽  
Steve Rockman ◽  
Vivian Leung ◽  
Hilda Lau ◽  
...  

AbstractInfluenza vaccines are utilised to combat seasonal and pandemic influenza. The key to influenza vaccination currently is the availability of candidate vaccine viruses (CVVs). Ideally, CVVs reflect the antigenic characteristics of the circulating virus, which may vary depending upon the isolation method. For traditional inactivated egg-based vaccines, CVVs are isolated in embryonated chicken eggs, while for cell-culture production, CVV’s are isolated in either embryonated eggs or qualified cell lines. We compared isolation rates, growth characteristics, genetic stability and antigenicity of cell and egg CVV’s derived from the same influenza-positive human clinical respiratory samples collected from 2008–2020. Influenza virus isolation rates in MDCK33016PF cells were twice that of eggs and mutations in the HA protein were common in egg CVVs but rare in cell CVVs. These results indicate that fully cell-based influenza vaccines will improve the choice, match and potentially the effectiveness, of seasonal influenza vaccines compared to egg-based vaccines.


2021 ◽  
Vol 2117 (1) ◽  
pp. 012025
Author(s):  
N H Rohiem ◽  
A Soeprijanto ◽  
O Penangsang ◽  
N P U Putra ◽  
R Defianti ◽  
...  

Abstract There are various types of fault that can occur in the distribution system network, so it is necessary to identify the location of the fault and isolate the fault in the area of the fault. The city of Surabaya is in preparation for the development of a smart city, so it is necessary to prepare a smart distribution system network system that can identify locations and isolate disturbed areas automatically. This paper describes the reconfiguration process to improve the value of losses in the system which results in a decrease in the value of total line losses after reconfiguration of 313.46 kW from 8 scenarios and includes the effect of adding solar energy to the existing network. The process of identifying the fault location and the isolation process on the Surabaya distribution system network in this paper uses the deep learning method. The fault location is determined based on the voltage and current profile of each bus in the system, while the isolation process is carried out by opening the switch closest to the fault area. In this process, deep learning can provide accurate fault location and isolation results for 6 fault tests.


Sign in / Sign up

Export Citation Format

Share Document