Interpretable diagnosis
Signal processing
Formal reasoning
Integration note.
The six uploaded modular files were integrated into the website as navigable module pages. Their presentation now matches the unified SRIS visual system and links directly to the broader Research, People, Publications, and Gallery sections.
Module resources
三个模块支柱
原始的三个模块支柱区域现已改为以视频为主的展示方式。每行展示一个外部视频，并保留对应的 PDF 资料入口，下方继续展示相关论文列表。
Temporal Logic Inference for Interpretable Fault Diagnosis of Bearings via Sparse and Structured Neural Attention
Gang Chen, Guangming Dong
ISA Transactions, Early Access, 2025
期刊
We propose a Sparse Temporal Logic Network for interpretable bearing fault diagnosis. The framework combines wavelet-based predicate extraction, sparse and structured neural attention, and temporal logic inference to deliver accurate diagnosis together with formal, human-readable explanations.
A Neural-Symbolic Network for Interpretable Fault Diagnosis of Rolling Element Bearings Based on Temporal Logic
Ruoyao Tian, Mengqian Cui, Gang Chen
IEEE Transactions on Instrumentation and Measurement, 73, 3515614
期刊
We develop a neural-symbolic learning architecture for interpretable rolling-bearing diagnosis that combines weighted signal temporal logic, predicate extraction, autoencoding, and timed failure propagation graphs to produce accurate and explainable fault decisions.
Interpretable Fault Diagnosis with Shapelet Temporal Logic: Theory and Application
Gang Chen, Yu Lu, Rong Su
Automatica, 142, 110350
期刊
We introduce shapelet temporal logic, a formal language that describes temporal relations among discriminative shapelets in sequential data. An incremental inference algorithm with theoretical guarantees is developed to obtain interpretable fault diagnosis rules for rolling element bearing signals.
Position in the lab
该模块在SRIS研究体系中的位置
模块定位
Interpretable Fault Diagnosis is presented as a reusable research block that can connect to papers, projects, talks, and demonstrations across the website.
Typical outputs
This module can aggregate theory papers, application case studies, tutorial materials, project narratives, and visual evidence under one stable page structure.
