Research Projects
Whole-System Approach to Railway Asset Management
A research project that investigates the interdependencies among railway assets and establishes a whole-system approach to railway asset management.
Machine Learning Approach to Redefining Risk in Railway Drainage Systems
Reframed drainage risk with data-driven learning methods.
A Hierarchical Bayesian Network for Modelling Multi Component Drainage Effects on Railway Track Deterioration
Established a probabilistic view of component-level drainage-driven deterioration.
Railway track performance prediction considering track-drainage interdependencies
Extended the track deterioration model to consider interdependencies with drainage assets.
Machine Learning Approach to Redefining Risk in Railway Drainage Systems
Reframed drainage risk with data-driven learning methods.
A Hierarchical Bayesian Network for Modelling Multi Component Drainage Effects on Railway Track Deterioration
Established a probabilistic view of component-level drainage-driven deterioration.
Railway track performance prediction considering track-drainage interdependencies
Extended the track deterioration model to consider interdependencies with drainage assets.
Knowledge-Driven Anomaly Detection and Diagnosis in Manufacturing Systems
A research project that evolved from fault-cause diagnosis into maintenance-record modeling, graph learning, and upstream anomaly detection.
A framework to support failure cause identification in manufacturing systems through generalization of past FMEAs
Established fault-cause diagnosis by combining fault knowledge and structural knowledge.
Description method and failure ontology for utilizing maintenance logs with FMEA in failure cause inference of manufacturing systems
Extended the diagnosis workflow with maintenance records and failure ontology.
Fault Cause Identification across Manufacturing Lines through Ontology-Guided and Process-Aware FMEA Graph Learning with LLMs
Expanded inference with LLMs and graph learning.
FBS model-based maintenance record accumulation for failure-cause inference in manufacturing systems
Introduced FBS-based data input and record accumulation.
A spatio-temporal anomaly detection system to support understanding of abnormal phenomena in automated manufacturing lines
Moved the pipeline upstream to anomaly detection before diagnosis.
A framework to support failure cause identification in manufacturing systems through generalization of past FMEAs
Established fault-cause diagnosis by combining fault knowledge and structural knowledge.
Description method and failure ontology for utilizing maintenance logs with FMEA in failure cause inference of manufacturing systems
Extended the diagnosis workflow with maintenance records and failure ontology.
FBS model-based maintenance record accumulation for failure-cause inference in manufacturing systems
Introduced FBS-based data input and record accumulation.
Fault Cause Identification across Manufacturing Lines through Ontology-Guided and Process-Aware FMEA Graph Learning with LLMs
Expanded inference with LLMs and graph learning.
A spatio-temporal anomaly detection system to support understanding of abnormal phenomena in automated manufacturing lines
Moved the pipeline upstream to anomaly detection before diagnosis.