Interpretable fault diagnosis with shapelet temporal logic: Theory and application

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. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a shapelet temporal logic to describe the temporal properties of shapelets, and an incremental algorithm is used to find the optimal logic expression for fault diagnosis. A case study on rolling element bearing fault diagnosis demonstrates the high accuracy of the proposed method.