AD-STGN for RCA in CMMS
Description
This repository provides the code and data implementation for the AD-STGN model, designed for Root Cause Analysis (RCA) in complex, nonlinear, and highly coupled continuous manufacturing processes. Datasets Overview: To validate the effectiveness of the framework, experiments are conducted on two widely-recognized cyber-physical benchmarks and one real-world industrial case study. 1. Tennessee Eastman Process (TEP) The TEP dataset is a widely adopted benchmark for RCA, simulating a complex, nonlinear continuous process. System Network: Spatial graph comprising N=51 topological nodes (40 continuous sensor measurements, 11 manipulated control actions). Training Set: 230,500 steady-state samples (used exclusively to prevent data leakage and for causal graph extraction). Testing Set: Mixed sequences constructed by stitching 1,081 steady-state testing samples and 800 faulty testing samples. 2. Secure Water Treatment (SWaT) Operational data collected from a raw water purification plant across six physical stages (raw water supply, chemical dosing, ultrafiltration, dechlorination, reverse osmosis, and backwash). System Network: N=50 input nodes (24 continuous sensors, 26 discrete actuators) and 1 prediction target. Training Set: 496,800 steady-state samples representing normal behavior (collected over 7 days). Testing Set: 449,919 samples containing anomalous events (collected over 4 days of cyber-physical attacks). 3. Real-world Case Study: Injection Molding Production Line Deployed on a real-world injection molding production line of a leading global Automotive Electronics manufacturer in Tianjin. Data was collected from Manufacturing Execution Systems and IoT sensors between February 2, 2025, and March 14, 2025. Process Stages: Clamping, injection, holding, cooling, ejection, and robot picking/placing. System Network: 66 continuous sensor measurements (granular process dynamics) and 7 discrete control actions (machine setpoints and equipment configurations). Terminal State Indicator: X66 (part temperature when placed by the robotic arm). It integrates the cumulative thermal-mechanical history. Deviations correlate with terminal Injection Molding Process Defect (PMT) issues (e.g., internal bubbles, warpage, incomplete cooling). Data Split: Training Set: 88,000 normal steady-state samples. Validation Set: 22,614 normal samples. Testing Set: Chronological sequence where anomalies start from the 75th sample, representing a transition from normal operation to a PMT outbreak.
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Institutions
- Tianjin UniversityTianjin, Tianjin