Arc fault detection based on temporal analysis

2014 ◽  
Author(s):  
Jinmi Lezama ◽  
Patrick Schweitzer ◽  
Serge Weber ◽  
Etienne Tisserand ◽  
Patrice Joycux
Keyword(s):  
Fault Detection ◽  
Temporal Analysis

Twitter Usage Using Common Reference to Tinnitus

2020 ◽  
Vol 29 (2) ◽  
pp. 206-217
Author(s):  
Jianyuan Ni ◽  
Monica L. Bellon-Harn ◽  
Jiang Zhang ◽  
Yueqing Li ◽  
Vinaya Manchaiah
Keyword(s):  
Nonprofit Organizations ◽  
Temporal Trends ◽  
Temporal Analysis ◽  
Cross Sectional ◽  
For Profit ◽  
Common Reference ◽  
Personal Experiences ◽  
Twitter Users ◽  
Sectional Analysis ◽  
Share Information

Objective The objective of the study was to examine specific patterns of Twitter usage using common reference to tinnitus. Method The study used cross-sectional analysis of data generated from Twitter data. Twitter content, language, reach, users, accounts, temporal trends, and social networks were examined. Results Around 70,000 tweets were identified and analyzed from May to October 2018. Of the 100 most active Twitter accounts, organizations owned 52%, individuals owned 44%, and 4% of the accounts were unknown. Commercial/for-profit and nonprofit organizations were the most common organization account owners (i.e., 26% and 16%, respectively). Seven unique tweets were identified with a reach of over 400 Twitter users. The greatest reach exceeded 2,000 users. Temporal analysis identified retweet outliers (> 200 retweets per hour) that corresponded to a widely publicized event involving the response of a Twitter user to another user's joke. Content analysis indicated that Twitter is a platform that primarily functions to advocate, share personal experiences, or share information about management of tinnitus rather than to provide social support and build relationships. Conclusions Twitter accounts owned by organizations outnumbered individual accounts, and commercial/for-profit user accounts were the most frequently active organization account type. Analyses of social media use can be helpful in discovering issues of interest to the tinnitus community as well as determining which users and organizations are dominating social network conversations.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

O tym, co minęło, lecz nie zostało zapomniane: Badania archeologiczne na terenie byłego obozu zagłady w Treblince

2012 ◽  
pp. 83-118
Author(s):  
Caroline Sturdy Colls
Keyword(s):  
Temporal Analysis ◽  
Archaeological Survey ◽  
Mass Graves ◽  
Non Invasive ◽  
Physical Evidence ◽  
Spatial And Temporal Analysis ◽  
The Holocaust ◽  
Public Consciousness ◽  
Level Of Knowledge ◽  
Answering Questions

Public impression of the Holocaust is unquestionably centred on knowledge about, and the image of, Auschwitz-Birkenau – the gas chambers, the crematoria, the systematic and industrialized killing of victims. Conversely, knowledge of the former extermination camp at Treblinka, which stands in stark contrast in terms of the visible evidence that survives pertaining to it, is less embedded in general public consciousness. As this paper argues, the contrasting level of knowledge about Auschwitz- Birkenau and Treblinka is centred upon the belief that physical evidence of the camps only survives when it is visible and above-ground. The perception of Treblinka as having been “destroyed” by the Nazis, and the belief that the bodies of all of the victims were cremated without trace, has resulted in a lack of investigation aimed at answering questions about the extent and nature of the camp, and the locations of mass graves and cremation pits. This paper discusses the evidence that demonstrates that traces of the camp do survive. It outlines how archival research and non-invasive archaeological survey has been used to re-evaluate the physical evidence pertaining to Treblinka in a way that respects Jewish Halacha Law. As well as facilitating spatial and temporal analysis of the former extermination camp, this survey has also revealed information about the cultural memory.

Multiple faults detection and isolation approach applied to a kraft recovery boiler

TAPPI Journal ◽  
2014 ◽  
Vol 13 (1) ◽  
pp. 33-41
Author(s):  
YVON THARRAULT ◽  
MOULOUD AMAZOUZ
Keyword(s):  
Fault Detection ◽  
Early Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Principal Component ◽  
Fault Isolation ◽  
Fault Detection And Isolation ◽  
Detection Scheme ◽  
Multiple Faults ◽  
Recovery Boiler

Recovery boilers play a key role in chemical pulp mills. Early detection of defects, such as water leaks, in a recovery boiler is critical to the prevention of explosions, which can occur when water reaches the molten smelt bed of the boiler. Early detection is difficult to achieve because of the complexity and the multitude of recovery boiler operating parameters. Multiple faults can occur in multiple components of the boiler simultaneously, and an efficient and robust fault isolation method is needed. In this paper, we present a new fault detection and isolation scheme for multiple faults. The proposed approach is based on principal component analysis (PCA), a popular fault detection technique. For fault detection, the Mahalanobis distance with an exponentially weighted moving average filter to reduce the false alarm rate is used. This filter is used to adapt the sensitivity of the fault detection scheme versus false alarm rate. For fault isolation, the reconstruction-based contribution is used. To avoid a combinatorial excess of faulty scenarios related to multiple faults, an iterative approach is used. This new method was validated using real data from a pulp and paper mill in Canada. The results demonstrate that the proposed method can effectively detect sensor faults and water leakage.

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