WHAT IS MACHINE LEARNING?
Machine Learning (ML) is not just a catchphrase in 2020. ML has transformed many industries over the past years and the world is just starting to realise its true potential.
Researchers interested in Artificial Intelligence (AI) wanted to investigate whether computers and machines could learn from data. The idea developed and ML was born from pattern identification and the belief that computers will one day have the ability to recognise these trends and perform specific actions and predictions.
The primary goal is to allow computers and machines to automatically learn and perform actions without human intervention or being explicitly programmed to do so.
There are two commonly used ML algorithms, Supervised Learning and Unsupervised Learning.
Supervised Learning is the act of providing the machine or computer a set of data to learn from, training it to build a reasonable model, and giving it new data to predict an outcome.
For example, you can provide the system a predefined acceptance criterion to learn, which can later be used to identify objects and detect anomalies from images taken by an inspection or mainteance team on site.
Unsupervised Learning, in contrast, is when the machine is provided with unclassified or unlabelled data which it then separates by identifying patterns or structures in the data.
Some well-known applications of this type include targeted ads, image segmentation, and document clustering based on content.
WHY IS IT IMPORTANT?
In industry, we increasingly collect large amounts of data from various sources. This leads to growing volumes and variations of the available data, increasing its complexity, which in turn makes it difficult to systematically review and analyse.
Machine Learning enables us to quickly and automatically produce models that can analyse larger and more complex data sets whilst also delivering faster and more accurate analysis and results.
“Machine learning could save the oil and gas industry as much as $50 billion in the coming decade”, according to McKinsey.
Nowadays, AI and ML technologies are rapidly developing which provides an opportunity for the energy industry to take steps forward toward in optimizing their data management capabilities.
HOW IS AIE UTILISING ARTIFICIAL INTELLIGENCE & MACHINE LEARNING?
AIE has always been proud of our data-driven approach towards integrity management. We continue to employ the latest technologies to fully harness the power of data and create an environment for engineering judgement to prosper.
We are engaging AI and ML in various aspects of our technology solutions across all equipment categories and as a business we are actively building our data science capabilities.
One Quick Example:
AIE is currently working to integrate AI and ML capabilities within our new Veracity Inspection software module. A ML algorithm is used to automatically extract, report, and compare historical inspection and maintenance data to identify patterns and detect anomalies, such as duplicates or large unrealistic variations in thickness readings. This solution will help to capture large data sets efficiently and build models that can drive quantitative condition assessments and predict critical outcomes throughout the asset lifecycle.
We are working on a number of exciting technology advancement projects which are targeted to cover all aspects of the integrity management cycle.
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