Data analysis in NDT: Key to defect management

Introduction

The evolution of industrial inspection technologies has ushered in an era where data analysis has become a critical tool for Non-Destructive Testing (NDT). This integration has transformed how industries manage asset integrity, particularly in the context of condition-based maintenance and predictive analysis.

Through the digitization of inspections and the advancement of tools like machine learning and Artificial Intelligence (AI), it is now possible to detect, classify, and predict defects with unprecedented accuracy.

Why is NDT data quality important?

Using NDT, also known as non-destructive testing, materials and components are evaluated without compromising their functionality. However, the true value of these tests—and the effectiveness of a predictive analysis system—lies in the quality and depth of the collected data. Incomplete, noisy, or poorly calibrated data can lead to incorrect conclusions, undermining defect management. Therefore, it is crucial to ensure that inspection equipment is properly calibrated, and that technicians follow standardized procedures for data acquisition.

Software solutions for Nondestructive Testing data management facilitate automatic and accurate data collection, significantly improving the reliability of the results obtained.

Types of data useful for defect management

Effective defect management requires a variety of data from different sources. Among the most useful are:

• Raw signals: Data directly obtained from equipment such as ultrasound, radiography, eddy current testing, etc.
• Inspection metadata: Information about operating conditions, date, technician who performed the inspection, and type of equipment used.
• Inspection history: Chronological sequence of data collected during different inspections, enabling assessment of defect evolution over time.
• Component health indicators: Such as crack growth rate, changes in density, or reflectivity of NDT signals.

The integration of all this data into a centralized NDT data analysis system provides a comprehensive view of asset condition, which is essential for predictive maintenance.

Advantages of data analysis over visual assessment

Although visual inspection remains a valuable method in certain contexts, data analysis offers major advantages:

• Greater accuracy: AI algorithms can identify patterns invisible to the human eye.
• Traceability: Digital data can be recorded, compared, and audited in a structured way.
• Early detection: Predictive analysis allows for the anticipation of defect appearance or growth before they become critical failures.
• Cost reduction: Maintenance cycles are optimized through condition-based maintenance, avoiding unnecessary component replacements.

Is Artificial Intelligence necessary in NDT data analysis?

Not always, but artificial intelligence significantly enhances analysis effectiveness. AI allows for:

• Automated defect classification.
• Complex correlation of variables.
• Continuous improvement through machine learning.

However, in many cases, data visualization tools and descriptive statistics already provide considerable value. The key is selecting the right technology based on the system’s complexity and the goals of the analysis.

Integration with Condition-Based Maintenance

Condition-Based Maintenance (CBM) relies directly on the ability to interpret NDT data. By effectively correlating inspection results with operating conditions and inspection history, it is possible to define specific alert thresholds for each asset.

For example, a crack detected by ultrasound may not require immediate action if its growth rate is low and the component operates below its load limit. But if predictive analysis reveals accelerated propagation, a scheduled maintenance order is triggered.

This approach improves safety and optimizes resources, aligning maintenance activities with the actual needs of the assets.

Role of data visualization tools

Automation and data management incorporates predictive analytics, 3D modeling and real-time defect visualization for faster and more reliable inspections in various industries. Data visualization is significant in the interpretation of Nondestructive Testing results. Tools such as dashboards, heat maps, three-dimensional representations of inspected components and time series enable engineers and technicians:

• Detect trends over time.
• Identify high-criticality areas.
• Compare defects between different assets or locations.

These visualizations are important because they enhance communication among maintenance, quality, and asset management teams.

Industrial cases

• Petrochemical industry: Equipment such as heat exchangers, storage tanks, and process pipelines are inspected through digital NDT. The collected data feeds NDT data analytics platforms to anticipate failures due to corrosion under insulation or hydrogen-induced cracking, thus optimizing maintenance plans.
• Aerospace sector: The structural integrity of aircraft is constantly monitored using NDT methods such as Phased Array Ultrasonic Testing and Eddy Current Testing. AI algorithms help detect fatigue defects in critical zones, directly contributing to service reliability and flight safety.
• Power generation: Steam and gas turbines undergo periodic inspections using digital radiography and ultrasound. Through predictive analysis, remaining life cycles can be estimated, preventive shutdowns can be scheduled, and catastrophic risks can be minimized.
• Civil infrastructure: In bridges, dams, and tunnels, NDT data collected by embedded sensors or robotic inspections is integrated into condition-based maintenance platforms. Real-time data visualization facilitates decision-making during critical operations.

Challenges and considerations

The effective implementation of NDT data analysis faces several challenges:

• Data standardization: Integrating information from different NDT technologies requires standardized formats.
• Staff training: Technical training in the use of digital tools and interpretation of analytical results is essential.
• Data protection: Cybersecurity must be a priority, especially when systems are connected to industrial networks.
• Digitization of historical records: Most valuable data from previous inspections is stored in physical (paper) format. To fully leverage data analysis potential, this data must be scanned, processed with OCR technologies, and validated by experts before being integrated into digital defect management platforms.

Conclusion

Data analysis applied to Non-Destructive Testing is redefining inspection standards, enabling more precise, efficient, and proactive defect management. While artificial intelligence and machine learning offer additional advantages, their use is not strictly necessary to obtain tangible benefits. The critical factors are high-quality data, appropriate data visualization tools, and a clear integration strategy with condition-based maintenance.

References

1. ASNT. (2020). Nondestructive testing handbook: Volume 10 – Nondestructive testing overview (3rd ed.). American Society for Nondestructive Testing.