From Inspection to Prediction: Integrating NDT, RBI, and AI for Industrial Asset Integrity Management

This paper presents an evidence-based review of Non-Destructive Testing (NDT) and its role in ensuring the integrity of high-consequence industrial assets across aerospace structures, overhead cranes, high-pressure systems, and bridge closure joints. Drawing on global safety statistics from organizations such as ILO, BLS, and NBBI, the study highlights the significant human and economic impact of inspection deficiencies and establishes the need for integrated inspection strategies.

Three primary contributions are introduced. First, a unified framework linking NDT method selection with failure-mode capability across multiple asset classes is developed, enabling structured inspection planning. Second, a fully worked quantitative Risk-Based Inspection (RBI) case study, based on API RP 580/581, demonstrates the identification of dominant damage mechanisms and optimized inspection intervals for a refinery pressure vessel. Third, an AI-enabled NDT pipeline is validated using a simulated phased array ultrasonic testing (PAUT) dataset, achieving 92.9% classification accuracy and providing confusion-matrix-based performance insights.

In addition, technical models including Paris law for crack growth, Probability of Detection (POD) characterization, and machine learning performance metrics are integrated to support quantitative inspection program design. The results demonstrate that combining NDT, RBI, and AI enables a transition from periodic inspection toward predictive, risk-informed asset integrity management.

This integrated framework provides a practical pathway for industry adoption of NDT 4.0 and supports data-driven decision-making for enhanced safety, reliability, and cost efficiency.