Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
An Online Algorithm for Segmenting Time Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Approximate Queries and Representations for Large Data Sequences
Approximate Queries and Representations for Large Data Sequences
Information inconsistencies detection using a rule-map technique
Expert Systems with Applications: An International Journal
Review of fault diagnosis in control systems
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Fault diagnosis in railway track circuits using Dempster-Shafer classifier fusion
Engineering Applications of Artificial Intelligence
Determining state transition probabilities using multi-objective optimisation
MS '08 Proceedings of the 19th IASTED International Conference on Modelling and Simulation
A knowledge-based architecture for distributed fault analysis in power networks
Engineering Applications of Artificial Intelligence
A framework for on-line trend extraction and fault diagnosis
Engineering Applications of Artificial Intelligence
Dynamic independent component analysis approach for fault detection and diagnosis
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence
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Dynamic trend analysis is an important technique for fault detection and diagnosis. Trend analysis involves hierarchical representation of signal trends, extraction of the trends, and their comparison (estimation of similarity) to infer the state of the process. In this paper, an overview of some of the existing methods for trend extraction and similarity estimation is presented. A novel interval-halving method for trend extraction and a fuzzy-matching-based method for similarity estimation and inferencing are also presented. The effectiveness of the interval halving and trend matching is shown through simulation studies on the fault diagnosis of the Tennessee Eastman process. Industrial experiences on the application of trend analysis technique for fault detection and diagnosis is also presented followed by a discussion on outstanding issues and solution approaches.