Sensor Data Correlation System Development (Design Phase).

1977 ◽  
Author(s):  
James L. Burrows ◽  
Thomas Moore ◽  
Rossin III ◽  
Senard Brian ◽  
Hubbard
Keyword(s):  
System Development ◽  
Sensor Data ◽  
Design Phase ◽  
Data Correlation ◽  
Correlation System

Pig Farm Environment Sensor Data Correlation and Heatmap Analysis for Predicting Sensor Remaining Useful Life✱

2020 ◽  
Author(s):  
Ji Hoon Lee ◽  
Seung Min Oh ◽  
Yeong Gwang Kim ◽  
Dong Su Lee ◽  
Akm Ashiquzzaman ◽  
...  
Keyword(s):  
Remaining Useful Life ◽  
Sensor Data ◽  
Data Correlation ◽  
Pig Farm ◽  
Useful Life

scholarly journals A Correlation-Change Based Feature Selection Method for IoT Equipment Anomaly Detection

2019 ◽  
Vol 9 (3) ◽  
pp. 437 ◽  
Author(s):  
Shen Su ◽  
Yanbin Sun ◽  
Xiangsong Gao ◽  
Jing Qiu ◽  
Zhihong Tian
Keyword(s):  
Feature Selection ◽  
Anomaly Detection ◽  
False Negative ◽  
Feature Selection Method ◽  
Window Size ◽  
Selection Method ◽  
Sensor Data ◽  
Data Correlation ◽  
The Right ◽  
Sensor Clustering

Selecting the right features for further data analysis is important in the process of equipment anomaly detection, especially when the origin data source involves high dimensional data with a low value density. However, existing researches failed to capture the fact that the sensor data are usually correlated (e.g., duplicated deployed sensors), and the correlations would be broken when anomalies occur with happen to the monitored equipment. In this paper, we propose to capture such sensor data correlation changes to improve the performance of IoT (Internet of Things) equipment anomaly detection. In our feature selection method, we first cluster correlated sensors together to recognize the duplicated deployed sensors according to sensor data correlations, and we monitor the data correlation changes in real time to select the sensors with correlation changes as the representative features for anomaly detection. To that end, (1) we conducted curve alignment for the sensor clustering; (2) we discuss the appropriate window size for data correlation calculation; (3) and adopted MCFS (Multi-Cluster Feature Selection) into our method to adapt to the online feature selection scenario. According to the experiment evaluation derived from real IoT equipment, we prove that our method manages to reduce the false negative of IoT equipment anomaly detection of 30% with almost the same level of false positive.

Detection Efficiency Analysis of Atmospheric Discharges Using AS3935 Sensor : Data Correlation of LINET Network

2019 ◽  
Author(s):  
Armando Heilmann ◽  
Elielton Matias da Silva ◽  
Horacio Tertuliano Filho ◽  
Joice Cristine Schuhmann ◽  
Augusto Mathias Adams ◽  
...  
Keyword(s):  
Detection Efficiency ◽  
Efficiency Analysis ◽  
Sensor Data ◽  
Data Correlation ◽  
Atmospheric Discharges

Hybrid gene expression programming-based sensor data correlation mining

2017 ◽  
Vol 14 (1) ◽  
pp. 34-49
Author(s):  
Lechan Yang ◽  
Zhihao Qin ◽  
Kun Wang ◽  
Song Deng
Keyword(s):  
Gene Expression ◽  
Gene Expression Programming ◽  
Sensor Data ◽  
Data Correlation ◽  
Hybrid Gene ◽  
Correlation Mining
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