AI and Machine Learning for NDTNDTMA Conference

February 2019

Presentation Outline

• AI definition • Examples and Notable applications in the

marketplace today• Application in the NDT field• Barriers• Conclusion• Next Steps

The Business Case for Embracing Technology in the NDT Community

The case for why this is important to the NDT community:• NDT service market, globally and USA: $8.5B / $3.4B respectively

• NDT technical staffing level in USA (in-house and out sourced) is 26,000. Predicted to rise to 33,000 by 2023

• Aging workforce issue is particularly evident in the NDT field

• Industry margins will be challenged due to labor cost inflation

• One industry report documents labor as a percent of revenue has grown from 46% in 2011 to 51% in 2017

• And finally:

• Business valuation multiples for scalable, technology based industrial firms are 7 – 10 X vs NDT service firm multiples in the 5 – 7 X EBITDA range

AI / ML / DL – Which is it ?

Some Quick Examples

Elements of Artificial Intelligence

Common Applications for AI

Typical Applications

Let’s start with the most obvious …

• Autonomous driving

• Chatbots / Customer service

• HR / Recruiting Automation

• Pharmaceutical

• Journalism

• Household appliances

But Applications Cross All Industries

NDT Case Studies

AI / ML Case Study #1Workpad / TechKnowServ team

• Goal: Predict component remaining life • Participants:

• TechKnowServ (NDT service provider)

• Workpad, LLC (Technology enabler)

• Inspection framework:• RFID tagged assets

• Danatronics Ultrasonic thickness data collection with wireless data transfer to Cloud environment

• Web application for display of thickness data

• Integration of Machine Learning• Workpad integrated Amazon Machine Learning Service

• Use ML algorithms to discover patterns in data and construct predictive models.

• Use output from model to make prediction of remaining life.

AI / ML Case Study #2Ultrasonic Phased Array Data Analysis

• Goal: Reduce PA data analysis time / Improve consistency of analysis

• Participants• Veriphase

• Regional NDT service provider

• Inspection framework:• Typical PAUT data collection with Olympus OmniPC

• Veriphase ADT program for data processing

• Integration of Machine Learning• Rules built around 27 essential variables

• Proprietary algorithms for geometry classification.

AI / ML Case Study #2Ultrasonic Phased Array Data Analysis

Key Benefits:• Machine Learning protocols allow ADT to:

• Serve as the initial screening / decision tool

• Allow ADT to be used as a training tool

• Can be used as a QA data check

• Achieve 90% reduction in analysis time.

• Software will allow PA data analysis at a rate faster than acquisition rate.

• Real world example:• 30 – 45 min to acquire 500 mb of PA data in a 20’ weld

• Less than one minute to analyze and serve up only relevant indications for human interpretation.

Digital Radiography #3

• Goal: Automated discontinuity detection• Participants:

• BAM Federal Institute (Berlin Germany)

• Dept of Computer Science, Pontifical Catholic University of Chile

• Inspection framework:• Collection of 19,400 radiographs

• Digitized from conventional acetate film

• Integration of Machine Learning• University of Chile developed large scale, image database.

• Formed GD Xray to manage commercially accessible database of weld and casting images.

Medical / Industrial ParallelToday, one of the biggest problems facing physicians and clinicians in general is the overload of too much patient information to sift through. This rapid accumulation of electronic data is thanks to the advent of electronic medical records (EMRs) and the capture of all sorts of data about a patient that was not previously recorded, or at least not easily data mined. This includes imaging data, exam and procedure reports, lab values, pathology reports,

waveforms, data automatically downloaded from implantable electrophysiology devices, data transferred from the imaging and diagnostics systems themselves, as well as the information entered in the EMR, admission, discharge and transfer (ADT), hospital information system (HIS) and billing software. In the next couple years there will be a further data explosion with the use of bidirectional patient portals, where patients can upload their own data and images to their EMRs. This will include images shot with their phones of things like wound site healing to reduce the need for in-person follow-up office visits. It also will include medication compliance tracking, blood pressure and weight logs, blood sugar, anticoagulant INR and other home monitoring test results, and activity

tracking from apps, wearables and the evolving Internet of things (IoT) to aid in keeping patients healthy. Physicians liken all this data to drinking from a firehose because it is overwhelming. Many say it is very difficult or impossible to go through the large volumes of data to pick out what is clinically relevant or actionable. It is easy for things to fall through the cracks or for things to be lost to patient follow-up. This issue is further compounded when you add factors like increasing patient volumes, lower reimbursements, bundled payments and the conversion from fee-for-service to a fee-for-value reimbursement system. This is where artificial intelligence will play a key role in the next couple years.

AI will not be diagnosing patients and replacing doctors — it will be augmenting their ability to find the key, relevant data they need to care for a patient and present it in a concise, easily digestible format.

Excerpt from Image Technology news: “How Artificial Intelligence Will Change Medical Imaging”, Feb, 24, 2017

AI Adoption Barriers

• No industry effort to coordinate data standardization

• Lack of centralized, open source databases for training the computer / AI system

• Lack of clear protocol for labeling data (i.e. DICONDE for digital radiography)

• Labor hour driven service model provides a headwind.

Conclusions and Next Steps

• AI applications are mainstream in adjacent industries

• AI and ML application is nascent in Industrial NDT

– Lack of sufficient scale opportunity

• Commercially available tools like Amazon Machine Learning

service, and OpenVDNA Project1 make technology accessible at

low cost

• Key issue is access to normalized and large datasets

– Role for NDTMA / ASNT ??

• Engage external consultants that have proven success

• Start with small, quick wins to secure organizational buy in.1 – Ref. NDT.ORG, 10-31-18

Questions ?

Appendix

OpenVDNA Project

• Exascale Visual AI initiative to sequence Visual DNA from images using Volume Learning to enable new applications.

• The OpenVDNA project provides public resources for visual AI solutions, including a visual DNA catalog, visual AI search engine, and a visual AI toolkit.

• Visual DNA are taken from thousands of small pieces of images composing the fabric of the image.

• Visual DNA elements are analyzed and compared using over 16,000 feature metrics in four primary bases: Color, Shape, Texture and Glyphs.

• Visual DNA are learned and stored in the OpenVDNA catalog using Learning Agents to create structures representing higher-level objects, and then accessed via the OpenVDNA search engine and the OpenVDNA software toolkit.