From Data to Digital Twin
A four-phase roadmap for advancing technologies that combine academic research with industrial practice, built on field data and domain expertise. See where PILETA Co., Ltd. stands today and where it is heading.
4-Phase Technology Timeline
From a foundation of sensor data acquisition to a digital twin that fuses physics and data, follow PILETA's technology progression and the status of each phase.
Phase 1
Sensor data acquisition (OPC UA, TCP), test data management with PIMP (PILETA Intelligence Materials database Program), master data standardization (MDM), unified authentication and authorization (SSO)
Phase 2
AI-based condition diagnostics, deep learning materials analysis (98.3%), automatic damage calculation, advanced anomaly detection (Bayesian neural network, BNN)
Phase 3
Physics-based damage analysis, remaining life prediction with a Physics-Informed Neural Network (PINN), flow-accelerated corrosion (FAC) wall thinning prediction, automatic risk grade classification
Phase 4
Physics–data fusion (PINN), real-time life prediction, predictive maintenance optimization, Digital Twin implementation
Note: Status labels are qualitative. “Complete” means the foundation is in place; “In progress” means development is still advancing.
By Phase
Key technologies and representative projects for each phase.
Phase 1 · Data Foundation
CompleteEverything starts with data. The foundation for data acquisition, management, standardization and authentication is already in place.
Key elements
Sensor data acquisition (OPC UA, TCP), test data management with PIMP (PILETA Intelligence Materials database Program), master data standardization (MDM), unified authentication and authorization (SSO)
Representative projects
BSE (Boiler Safety Evaluation) · HRSG DAS · MDM · SSO
Phase 2 · Intelligence
In progressAccumulated data becomes intelligence. AI that reads microstructures classifies degradation grades with 98.3% accuracy.
Key elements
AI-based condition diagnostics, deep learning materials analysis (98.3%), automatic damage calculation, advanced anomaly detection (Bayesian neural network, BNN)
Representative projects
Creep microstructure classification AI · PIMP automated analysis
Phase 3 · PHM Hybrid
In progressPhysics meets AI to predict remaining life. The PINN hybrid calculates remaining life and wall thinning.
Key elements
Physics-based damage analysis, remaining life prediction with a Physics-Informed Neural Network (PINN), flow-accelerated corrosion (FAC) wall thinning prediction, automatic risk grade classification
Representative projects
PINN AI · BSE · FAC module
Phase 4 · Digital Twin
PlannedA digital twin of your equipment shows what lies ahead, advancing toward real-time life prediction and optimized predictive maintenance.
Key elements
Physics–data fusion (PINN), real-time life prediction, predictive maintenance optimization, Digital Twin implementation
Representative projects
Physics-informed AI · Hybrid model
Current Status
Phase 1 is complete, Phases 2 and 3 are under way in parallel, and Phase 4 is planned. On a solid data foundation, we are advancing intelligence and physics-based analysis at the same time.
Ready When Reliability Matters
The PILETA Test & Evaluation Center is accredited by the Korea Laboratory Accreditation Scheme (KOLAS) as a testing laboratory (Accreditation No. KT921). From testing and failure analysis to digital solution consultations, our engineers respond to you directly.
T. +82-42-368-0180 · pileta@pileta.co.kr
