Trend Analysis for Decision Support in Control Actions of Suspension Polymerization
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Computer Methods and Programs in Biomedicine
Review of fault diagnosis in control systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Extracting trend of time series based on improved empirical mode decomposition method
APWeb/WAIM'07 Proceedings of the joint 9th Asia-Pacific web and 8th international conference on web-age information management conference on Advances in data and web management
A framework for on-line trend extraction and fault diagnosis
Engineering Applications of Artificial Intelligence
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part III
An LSSVR-based algorithm for online system condition prognostics
Expert Systems with Applications: An International Journal
Artificial intelligence for industrial process supervision
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
On-line extraction of qualitative movements for monitoring process plants
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
An intelligent supervision system for open loop controlled processes
Journal of Intelligent Manufacturing
| Hi-index | 0.00 |
This paper presents an effective trend extraction procedure, based on a simple, yet powerful, representation. Its usefulness for complex system monitoring and decision support is illustrated by three examples. The method extracts semi-qualitative temporal episodes on-line, from any univariate time series. Three primitives are used to describe the episodes: {Increasing, Decreasing, Steady}. The method uses a segmentation algorithm, a classification of the segments into seven temporal shapes and a temporal aggregation of episodes. It acts on noisy data, without prefiltering. The first illustration is devoted to decision support in intensive care units. The signals contain information and noise at very different frequencies, and smoothing must not mask some interesting high-frequency data features. The second illustration is dedicated to a food industry process. On-line trends of key variables represent a very useful monitoring tool to control the end product quality despite high variations of raw materials at the input and a long delay. The last example concerns operator support and predictive maintenance. The results issued from a diagnostic module are complemented by the extrapolation of the key variable trends, which gives an idea of the time left to repair or reconfigure the process.