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How Machine Learning can improve Integrity Management

Machine-Learning

Conventional inspection work processes include several repetitive and time-consuming reporting activities to ensure accurate data is collected and stored in the asset owner’s CMMS. With the integration of an inspection software, the time from inspection to final report is shrunk by 30-50%, capturing and sharing data effortlessly in real time. While it has improved the efficiency of asset management, the emergence of machine learning presented an even greater potential to transform raw information into actionable value. In this article, we look at the practical implementations of these two technologies and how they can dramatically improve the activities performed within integrity management.


QUICK ACCESS TO DATA THROUGH IMAGE RECOGNITION

Axess has developed an inspection software to increase the efficiency of the inspection work process through conventional software solutions. To further improve its functionality, machine learning may be used for object detection on images and video. Machine learning is an artificial intelligence (AI) application that analyses large amounts of data and algorithms, learnt through observation to find patterns, and thereby foresee future issues based on historic data.

An example is during inspection of lifting equipment, machine learning can identify the objects from the images taken by the field technician. Finding the correct equipment type, model, acceptance criteria and applicable compliance rules in an automated fashion reduces the time used on reporting and accessing relevant data.

Pipe Inspection_image recognition
CONDITION EVALUATION

With machine learning paired with inspection software, it is possible to derive condition evaluation from images taken by the inspector. Anomalies in data collected such as corrosion detection, cracks, spillage or leaks can be assessed and captured in real time by these tools for a quick comparison of the asset condition today with the previous inspection period. This subsequently determines the next course of action necessary in the asset management lifecycle. With this type of solution, it is possible to acquire more data and evaluate the technical condition based on quantitative analysis as opposed to the qualitative analysis done by field engineers and inspectors today.

RISK ANALYSIS

The knowledge acquired from condition evaluation leads to a thorough assessment of the damage, specifically, the probability of failure and consequence of failure. To improve the understanding and evaluation of risk, it is necessary to build a tool to estimate the probability of failure. Machine learning can be used to prepare models to predict the outcome from the input parameters and correlated historic results.

It is suggested to prepare models for prediction of probability and severity of degradation mechanism based on process information, materials and inspection history. This needs a large data set to produce a reliable and accurate inspection strategy: focusing on components with the highest risk, coverage and frequency of inspections. The result is a more optimized use of inspection budget to reduce the overall risk.

CONCLUSION

Inspection software, when paired with machine learning, can improve business services significantly—from defects identification to compliance and maintenance solutions. This innovative methodology will increase the rate of data capture, provide comprehensive real-time data comparison, and allow new types of data to be used for condition evaluation. At Axess, we leverage technology to continuously deliver accurate and reliable asset integrity solutions.

Currently, Axess has an ongoing project covering the profitability of machine learning, supported by the Norwegian Regional Research Fund and some academic and industry partners. The results will be presented at the SPE International Oilfield Corrosion Conference and Exhibition in Aberdeen 18-19 June 2018. We hope to see you there!

For future collaboration, contact us at  post@axessgroup.com

Contact Information

Molde
Axess AS
Oscar Hanssens vei 5
6415 MOLDE
+47 982 43 600
Aberdeen
Axess North Sea Ltd.
Units 6 & 19 Robert Leonard Centre
Howe Moss Drive, Kirkhill Industrial Estate, Dyce AB21 0GG, UK
(duty): +44(0) 7912212854
St. John's
Axess Baffin Inc.
702 Water Street, Suite 201
St. John's, Newfoundland
+1 709 758 7937
Bergen
Axess AS
Kokstadflaten 35
5257 Bergen
(duty): +47 982 43 600
Houston
Axess North America Inc
15915 Katy Freeway, Suite 501
77094 Houston, TX
+1 (281) 994-0367
Ciudad del Carmen
+1 832 970 3748
Stavanger
Axess AS
Koppholen 25, 4313 Sandnes
Stavanger, Norway
(duty): +47 982 43 600
Rio de Janeiro
Axess do Brasil Ltda
Rua Dezenove de Fevereiro
171 - Botafogo
CEP 22280-030 Rio de Janeiro
+55 (21) 4108-0532
Accra
Axess Petrorig Ghana Limited
3rd floor, The Pelican
8 Blohum Street, Dzorwulu, Accra, Ghana
+233 (0) 30 397 0548
Luanda
Rainha Ginga
127 Luanda, Angola
+27 82 202 5266
Cape Town
Axess Africa Ltd.
1 Nares Street
Observatory
7925 Cape Town
+27 82 202 5266
Trondheim
Axess AS
Nedre Bakklandet 58C
7014 Trondheim
(duty): +47 982 43 600
Orkanger
Axess AS
Grønøraveien 1
Orkanger
7300 Orkanger
(duty): +47 41 78 06 30
Oslo
Axess AS
Filipstad Brygge 1, 3rd Floor
Oslo, Norway
(duty): +47 982 43 600
Busan
Axess Offshore Korea LLC
#103 (Aju-dong Hyunjin Evervil) B1 Yang 1-gil, Geoji-Si
Gyeongsangnam-do, South Korea
+82 10 8929 2016
Mumbai
Axess Offshore (Mumbai)
Alpha, 2nd Floor Unit No. 201
Hiranandani Gardens Powai, Mumbai
India
Singapore
Axess Offshore Pte Ltd
Nordic European Centre #05-31 South Wing
3 International Business Park
609927 Singapore
+65 6908 4174
Perth
Axess Offshore Australia Pty Ltd
140 St Georges Terrace L28,
Perth WA 6000
+61 4 3373 0739
Perth
Axess Offshore Australia Pty Ltd
140 St Georges Terrace L28,
Perth WA 6000
+61 4 3373 0739
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