Temporal Constrained Dynamic Uncertain Causality Graph for Root Cause Analysis of Intermittent Faults

The diagnosis of intermittent faults is crucial in the field of maintenance support. However, most existing studies focus on the analysis of intermittent faults of single components, ignoring the more complex intermittent failures of equipment functions due to the coupling of multivariate anomalous states in the fault propagation process. Existing diagnostic methods based on fault propagation models, which mainly focus on one-dimensional temporal or logical relationships, fall short in representing and reasoning about intermittent faults caused by temporal and state coupling. In this paper, we propose a Temporal Constrained Dynamic Uncertain Causality Graph (TC-DUCG) model to fill this gap and effectively model intermittent faults. The model not only considers the probability of fault propagation among variables, but also integrates temporal constraints. It also presents a diagnostic reasoning process to investigate potential causes of intermittent faults. We provide an illustrative example to demonstrate the effectiveness of the proposed method in diagnosing intermittent faults.

Authors

Institutions

Publication Details

Journal
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Published
2024-08-29
DOI
https://doi.org/10.17531/ein/192169
Primary Topic
Risk and Safety Analysis
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Temporal Constrained Dynamic Uncertain Causality Graph for Root Cause Analysis of Intermittent Faults

Zun Liu, Xiangnan Shi, Jiang Long, Yangming Guo et al.
Eksploatacja i Niezawodnosc - Maintenance and Reliability
Risk and Safety Analysis
article

Temporal Constrained Dynamic Uncertain Causality Graph for Root Cause Analysis of Intermittent Faults

Zun Liu, Xiangnan Shi, Jiang Long, Yangming Guo, Qiqi Ma
article en

Abstract

The diagnosis of intermittent faults is crucial in the field of maintenance support. However, most existing studies focus on the analysis of intermittent faults of single components, ignoring the more complex intermittent failures of equipment functions due to the coupling of multivariate anomalous states in the fault propagation process. Existing diagnostic methods based on fault propagation models, which mainly focus on one-dimensional temporal or logical relationships, fall short in representing and reasoning about intermittent faults caused by temporal and state coupling. In this paper, we propose a Temporal Constrained Dynamic Uncertain Causality Graph (TC-DUCG) model to fill this gap and effectively model intermittent faults. The model not only considers the probability of fault propagation among variables, but also integrates temporal constraints. It also presents a diagnostic reasoning process to investigate potential causes of intermittent faults. We provide an illustrative example to demonstrate the effectiveness of the proposed method in diagnosing intermittent faults.

Eksploatacja i Niezawodnosc - Maintenance and ReliabilityVol. 27(1)
Northwestern Polytechnical University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 86%
Risk and Safety Analysis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.