Limiting the human error factor of phased array testing data analysis
Nondestructive evaluation (NDE) has progressively required a higher level of automatization to be able to handle the amounts of data generated with modern data acquisition systems. Data analysis, especially the detection and classification of defects and faults, has traditionally relied on the skills of experts and typically requires many hours of manual labor to get reliable results. As computer power has increased, many classification machine learning vision algorithms have become available to speed up this time-consuming process. These algorithms reduce errors and misclassifications due to human error. Intertek offers an automatic defect recognition system methodology for NDE data using artificial neural networks/deep learning and similar algorithms for pattern recognition-based automated-signal classification. This system is capable of training models using Intertek's own (and other) databases on NDE data. This approach can lead to improved efficiency and higher reliability of detection when analyzing large amounts of sensing, monitoring, and NDE data that will be beneficial to all users when reducing the tedious work required on traditional techniques applied in the NDE field.
Intertek has proven experience with advanced analytics using large amounts of data in numerous projects, including power plant monitoring data. Our group has used data analytics and machine learning to show the impact of cycling data on dissimilar metal weld joint failures in recent work.
Intertek's AwareTM platform is an inspection data management software that is currently tracking inspection data for piping, pressure vessels, turbines, boilers, etc. for more than 800 power plant units. This program includes risk-based inspection tools, corrosion monitoring, half-life calculations, inspection scheduling, and turnaround planning widgets to create recommendations and repair tracking systems. Aware has recently merged with Intertek's INGRID, a web-based analytical platform that utilizes the latest in data manipulation technology and features the most recent benchmarking algorithms to provide valuable insights into asset performance, efficiency, and reliability metrics.
Intertek's approach is split in several phases.
The goal of Asset Integrity Management (AIM) is to effectively manage corporate assets in order to gain maximum value, profitability and returns while safeguarding personnel, the community, and the environment. At Intertek our AIM team delivers trusted and innovative technical solutions that ensure the quality, safety and reliability of our clients' assets.
Dr. Gascón is a data scientist with expertise in evaluating large data sets from fossil and renewable energy power plants. He uses statistical analysis, mathematical modeling and machine learning techniques to generate predictive models to improve plant efficiency. Dr. Gascón earned a Ph.D. in Nuclear Physics developing detectors to study nuclear reactions. During a 2-year postdoc at Stanford and 3-year fellowship at Berkeley National Laboratory, he studied "How to improve the functionality of radiation detectors by applying extreme conditions and using novel techniques and materials," with the objective of increasing the sensitivity of these detectors for national defense applications. Dr. Gascón leverages his broad experience, programming skills and quantitative analytical background to solve a wide variety of problems involving large data sets in the power industry. He is the principal architect of Intertek's data analytics platform Ingrid. His current responsibilities include the development of COSTCOM®, a real-time cost analysis tool for power plant operations, WearMulti and OPCONTM, two multipurpose programs used in the nuclear industry. He also developed Windlife, a fully customizable web-application for wind farms featuring anomaly detection and remaining life estimation. Dr. Gascon has also developed programs and algorithms to predict the impact of the variability in power generation of renewable energy on the wear and tear of fossil fuel plants.
Terry Haigler brings a wealth of knowledge to Intertek with 13 years of experience in the power generation sector in both nuclear and non-nuclear inspections. He specializes in ultrasonic inspection technique looking for in-service damage of piping welds and systems constructed of stainless steel (304, 316), carbon steel, P22, P11, and P91 materials. All of his inspection services are performed in accordance with ASME Section V, XI, or ASME B31.1. Additional experience includes metallurgical replications looking for creep damage and extensive experience with NDE techniques including MT, PT, VT, and eddy current inspections.