Skip to Main content Skip to Navigation
Conference papers

Fault Diagnosis and Knowledge Extraction Using Fast Logical Analysis of Data with Multiple Rules Discovery Ability

Abstract : Data-based explanatory fault diagnosis methods are of great practical significance to modern industrial systems due to their clear elaborations of the cause and effect relationship. Based on Boolean logic, logical analysis of data (LAD) can discover discriminative if-then rules and use them to diagnose faults. However, traditional LAD algorithm has a defect of time-consuming computation and extracts only the least number of rules, which is not applicable for high-dimensional large data set and for fault that has more than one independent causes. In this paper, a novel fast LAD with multiple rules discovery ability is proposed. The fast data binarization step reduces the dimensionality of the input Boolean vector and the multiple independent rules are searched using modified mixed integer linear programming (MILP). A Case Study on Tennessee Eastman Process (TEP) reveals the superior performance of the proposed method in reducing computation time, extracting more rules and improving classification accuracy.
Complete list of metadatas

Cited literature [29 references]  Display  Hide  Download

https://hal.inria.fr/hal-02118838
Contributor : Hal Ifip <>
Submitted on : Friday, May 3, 2019 - 1:27:30 PM
Last modification on : Friday, May 3, 2019 - 2:52:02 PM
Document(s) archivé(s) le : Wednesday, October 9, 2019 - 12:22:49 PM

File

 Restricted access
To satisfy the distribution rights of the publisher, the document is embargoed until : 2021-01-01

Please log in to resquest access to the document

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Xiwei Bai, Jie Tan, Xuelei Wang. Fault Diagnosis and Knowledge Extraction Using Fast Logical Analysis of Data with Multiple Rules Discovery Ability. 2nd International Conference on Intelligence Science (ICIS), Nov 2018, Beijing, China. pp.412-421, ⟨10.1007/978-3-030-01313-4_44⟩. ⟨hal-02118838⟩

Share

Metrics

Record views

34