Stability Monitoring of Burning Structures Based on Fire-induced Vibration Monitoring

Author(s):  
Ziyad H. Duron
Author(s):  
Pallab Kumar Gogoi ◽  
Mrinal Kanti Mandal ◽  
Ayush Kumar ◽  
Tapas Chakravarty

2011 ◽  
Author(s):  
T. Poczęsny ◽  
K. Prokopczuk ◽  
P. L. Makowski ◽  
A. W. Domański

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
A. Lebret ◽  
P. Berton ◽  
V. Normand ◽  
I. Messager ◽  
N. Robert ◽  
...  

AbstractIn the last two decades, in France, Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) stabilization protocols have been implemented using mass vaccination with a modified live vaccine (MLV), herd closure and biosecurity measures. Efficient surveillance for PRRSV is essential for generating evidence of absence of viral replication and transmission in pigs. The use of processing fluid (PF) was first described in 2018 in the United States and was demonstrated to provide a higher herd-level sensitivity compared with blood samples (BS) for PRRSV monitoring. In the meantime, data on vertical transmission of MLV viruses are rare even as it is a major concern. Therefore, veterinarians usually wait for several weeks after a sow mass vaccination before starting a stability monitoring. This clinical study was conducted in a PRRSV-stable commercial 1000-sow breed-to-wean farm. This farm suffered from a PRRS outbreak in January 2018. After implementing a stabilisation protocol, this farm was controlled as stable for more than 9 months before the beginning of the study. PF and BS at weaning were collected in four consecutive batches born after a booster sow mass MLV vaccination. We failed to detect PRRSV by qPCR on PF and BS collected in a positive-stable breeding herd after vaccination with ReproCyc® PRRS EU (Boehringer Ingelheim, Ingelheim, Germany).


2021 ◽  
Vol 43 ◽  
pp. 2290-2295
Author(s):  
N. Nithyavathy ◽  
S. Arun Kumar ◽  
K.A. Ibrahim Sheriff ◽  
A. Hariram ◽  
P. Hare Prasaad

2021 ◽  
Vol 30 (1) ◽  
pp. 677-688
Author(s):  
Zhenzhuo Wang ◽  
Amit Sharma

Abstract A recent advent has been seen in the usage of Internet of things (IoT) for autonomous devices for exchange of data. A large number of transformers are required to distribute the power over a wide area. To ensure the normal operation of transformer, live detection and fault diagnosis methods of power transformers are studied. This article presents an IoT-based approach for condition monitoring and controlling a large number of distribution transformers utilized in a power distribution network. In this article, the vibration analysis method is used to carry out the research. The results show that the accuracy of the improved diagnosis algorithm is 99.01, 100, and 100% for normal, aging, and fault transformers. The system designed in this article can effectively monitor the healthy operation of power transformers in remote and real-time. The safety, stability, and reliability of transformer operation are improved.


Sign in / Sign up

Export Citation Format

Share Document