applying business and artificial intelligence (bi/ai ...€¦ · applying business and artificial...

Applying Business and Artificial Intelligence (BI/AI) concepts in optimized, risk-based

inspection and maintenance of power and process plants components

45th MPA SeminarOctober 1-2, 2019

A. Jovanovic 1 , S. Chakravarty 1, T. Rosen 1

1 Steinbeis Advanced Risk Technologies, Germany

STEINBEIS ADVANCED RISK TECHNOLOGIES

The Industry 4.0 (“digitalization”): in the area of risk-based maintenance and inspection of power and process plants components.

Enormous increase in digital, but not the people dealing with them.

Difficulty in extracting the most relevant outputs and results out of the data.

How to “learn from past experience (cases)”

Potential application of: Business Intelligence (BI), Artificial Intelligence (AI) assisted tools for the optimization of future inspection and maintenance plans.

Intro


What has changed 1996 to 2019? RBI 1998:

4 basic risk outcomes - flammable event, toxic releases, major environmental damage, and business interruption losses.

Risk: Probability x Consequence

Both a qualitative and quantitative process for understanding and reducing the risks associated with operating pressure equipment

RBI 2018:the same & more > safety, business, environment, reputation, … WIDE ACCEPTANCE & NEW UNDERSTANDING OF RISK!

Context #1 RBI: More than 20+ years “around”


Context #2 RBI: AI, KI, ML, DL, NN, … DBA


Despite the age of big data, 71% of organizations still rely on a single data source to analyze asset performance and risk management.

39% of asset managers already implement advanced digital methods of maintenance and risk management, of which 26% are using risk-based inspection (RBI) and 13% are using reliability centred maintenance (RCM).

Advanced digital methods: Big data, AI and machine learning are the top digital technologies

Present Scenario

https://www. https://www.oilandgasmiddleeast.com/products-services/35067-71-of-oil-and-gas-asset-performance-and-risk-management-decisions-still-rely-on-a-single-data-source

https://www.vdi-wissensforum.de/weiterbildung-maschinenbau/maintenance

https://www.oilandgasmiddleeast.com/products-services/35067-71-of-oil-and-gas-asset-performance-and-risk-management-decisions-still-rely-on-a-single-data-source


VDI – Inspections in the age of digitization -Competitive advantage instead of cost factor

https://www.vdi-wissensforum.de/weiterbildung-maschinenbau/maintenance/

For a long time, inspections were seen as a (burdensome) cost factor in companies. But new technologies and developments enable companies to secure real competitive advantages through a properly designed inspection strategy.

INSPECTION AS DATA SOURCE!1. DATEN AS AN „ASSET“!

VDI-Priorities:• Predictive Maintenance• Industrial IOT/IOP• Maintenance 4.0 Strategies

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https://www.vdi-wissensforum.de/weiterbildung-maschinenbau/maintenance/


Data as Asset - RBI System and Digitization 4.0

Other systems

Database - Warehouse

RBI System

Data analysis (AI)OPTIMIZED INSPECTION AND

OPERATION (4.0)


Potential solution – Use of Business Intelligence (BI)

User 1

User 2

User 3

User 4

Business Intelligence (BI) based -

Interface

Financese.g: SAP

Maintenancee.g: SAP (AA)

Documentatione.g: IET

Managemente.g: IMS

Other Plant Systems

...

• "Data warehouse": each analysis saved on DB.

• Input data and Results for each :ComponentSystemPlant

Web –based solution

• Plant data• Components• Inspection Data• RBI Analyses• Users

Cloud Database

(SQL) Web user interface

Risk vs. Time RiskMap

(Excel-based) solution

RBI Scorecard (Excel )

• "File-based": each analysis, a seperate excel sheet.

• Change control: only possible by file naming & uploading the excel -files to a central database1 2 3


Integration of Data, Analytics and BI

• Dynamic “real-time”• Analytics• Rules• Dependencies

• ....

• Plant data• Components• Inspection Data• RBI Analyses• Users

Cloud Database(SQL)

User

Risk/Business Intelligence

• Tailor-made• Drill-down data levels


Power BI Dashboard (1/2)

Accessible link (with a PowerBI account)

Cat_S (CoF vs PoF)

https://app.powerbi.com/reportEmbed?reportId=0f5c00b1-3aa2-4509-ba7e-cb88a170df13&autoAuth=true&ctid=de8151da-a3a9-4734-9a3e-80153f6411fe&config=eyJjbHVzdGVyVXJsIjoiaHR0cHM6Ly93YWJpLXdlc3QtZXVyb3BlLWQtcHJpbWFyeS1yZWRpcmVjdC5hbmFseXNpcy53aW5kb3dzLm5ldCJ9


Power BI Dashboard (2/2)

Accessible link (with a PowerBI account)https://app.powerbi.com/groups/me/reports/0f5c00b1-3aa2-4509-ba7e-cb88a170df13/ReportSectionef9d48a09750d2d13199?ctid=de8151da-a3a9-4734-9a3e-80153f6411fe&openReportSource=ReportInvitation

https://app.powerbi.com/groups/me/reports/0f5c00b1-3aa2-4509-ba7e-cb88a170df13/ReportSectionef9d48a09750d2d13199?ctid=de8151da-a3a9-4734-9a3e-80153f6411fe&openReportSource=ReportInvitation


Machine learning (ML) is an application of artificial intelligence (AI)

Able to automatically learn and improve from experience without being explicitly programmed

Focused on the development of computer programs that can access data and use it learn for themselves

AI methods: Machine Learning

Source: https://mapr.com/blog/artificial-intelligence-and-machine-learning-what-are-they-and-why-are-they-important/

https://mapr.com/blog/artificial-intelligence-and-machine-learning-what-are-they-and-why-are-they-important/


Variables considered:1. Nominal diameter2. Hoop Stress3. Design Temperature4. Risk_Safety 5. Risk_Business6. Damage mechanism (DM)

Number of data points: 2,600 data points over the 6 variables.

Algorithm: CARET library Platform(IDE): R Studio Processing time: 1+ hour using Intel i7 7500U dual core

Case Study: Use of ML algorithm on RBI data


The CARET (Classification And REgression Training) is a set of functions that attempt to streamline the process for creating predictive models.

The package tools contain: Unified interface for modelling and prediction Data splitting Pre-processing Feature selection Model tuning using resampling Variable importance estimation Parallel processing: computational efficiency

CARET package on R


2,600 data points over 6 variables (used for training model)

Results (1/3)


46 data points used for prediction

Results (2/3)


Result (3/3)

The algorithm was able to correctly predict the correct DM mechanisms 89% of the time.

100 9893

87.5 86.3

49

0

20

40

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100

Corrosionfatigue

Creep Mechanicalfatigue

Pittingcorrosion

Thermalfatigue

Creep fatigue

% of Correct Predictions distributed by DM


Tools: Deep learning


Case Study: Use ML algorithm on RBI data

Steps involved

Source: https://i.stack.imgur.com/osBuF.png

https://i.stack.imgur.com/osBuF.png


Training data and predicted data


Tools: Why R?


Use big data, also for RBI … with understanding … in order to trust them and the analysis based on them

Conclusions

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