Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163544
Title: Interpretable fault diagnosis with shapelet temporal logic: theory and application
Authors: Chen, Gang
Lu, Yu
Su, Rong
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2022
Source: Chen, G., Lu, Y. & Su, R. (2022). Interpretable fault diagnosis with shapelet temporal logic: theory and application. Automatica, 142, 110350-. https://dx.doi.org/10.1016/j.automatica.2022.110350
Project: ICP1900093 
Journal: Automatica
Abstract: Shapelets are discriminative subsequences of sequential data that best predict the target variable and are directly interpretable, which have attracted considerable interest within the interpretable fault diagnosis community. Despite their immense potential as a data mining primitive, currently, shapelet-based methods ignore the temporal properties of shapelets. This paper presents a shapelet temporal logic, which is an expressive formal language to describe the temporal properties of shapelets. Moreover, an incremental algorithm is proposed to find the optimal logic expression with formal and theoretical guarantees, and the obtained logic expression can be used for fault diagnosis. Additionally, a case study on rolling element bearing fault diagnosis shows the proposed method can diagnose and interpret faults with high accuracy. Comparison experiments with other logic-based and shapelet-based methods illustrate the proposed method has better interpretability at the cost of computation efficiency.
URI: https://hdl.handle.net/10356/163544
ISSN: 0005-1098
DOI: 10.1016/j.automatica.2022.110350
Schools: School of Electrical and Electronic Engineering 
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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