Paper Info
Title
Ensemble Based Real-Time Adaptive Classification System for Intelligent Sensing Machine Diagnostics
Abstract
The deployment of a sensor node to manage a group of sensors and collate their readings for system health monitoring is gaining popularity within the manufacturing industry. Such a sensor node is able to perform real-time configurations of the individual sensors that are attached to it. Sensors are capable of acquiring data at different sampling frequencies based on the sensing requirements. The different sampling rates affect power consumption, sensor lifespan, and the resultant network bandwidth usage due to the data transfer incurred. These settings also have an immediate impact on the accuracy of the diagnostics and prognostics models that are employed for system health monitoring. In this paper, we propose a novel adaptive classification system architecture for system health monitoring that is well suited to accommodate and take advantage of the variable sampling rate of sensors. As such, our proposed system is able to yield a more effective health monitoring system by reducing the power consumption of the sensors, extending the sensors' lifespan, as well as reducing the resultant network traffic and data logging requirements. We also propose an ensemble based learning method to integrate multiple existing classifiers with different feature representations, which can achieve significantly better, stable results compared with the individual state-of-the-art techniques, especially in the scenario when we have very limited training data. This result is extremely important in many real-world applications because it is often impractical, if not impossible, to hand-label large amounts of training data.
Year
DOI
Venue
2012
10.1109/TR.2012.2194352
IEEE Transactions on Reliability
Keywords
Field
DocType
system health monitoring,feature representation,adaptive classification system architecture,ensemble based learning method,network traffic,power consumption,intelligent sensing machine diagnostics,prognostics model,production engineering computing,sampling rate,sensor node deployment,real-time configuration,manufacturing industries,diagnostics model,ensemble based real-time adaptive classification system,sensor data classification,adaptive classifiers,sampling frequencies,manufacturing industry,ensemble learning,sensor lifespan,classifiers,health monitoring system,condition monitoring,data logging,sampling methods,data driven diagnostics and prognostics,network bandwidth usage,data transfer,wireless sensor network,sampling frequency,classification system,data model,data models,predictive models,prediction model,vibrations,real time,wireless sensor networks,support vector machine,support vector machines
Sensor node,Data modeling,Data logger,Data mining,Prognostics,Real-time computing,Condition monitoring,Systems architecture,Ensemble learning,Wireless sensor network,Mathematics,Reliability engineering
Journal
Volume
Issue
ISSN
61
2
0018-9529
Citations 
PageRank 
References 
3
0.73
8
Authors
5
Name
Order
Citations
PageRank
Minh Nhut Nguyen122416.44
Chunyu Bao2163.31
Kar Leong Tew3242.41
Sintiani Dewi Teddy441.55
Minh Nhut Nguyen51837112.04