Driving reliability and improving maintenance outcomes with machine learning

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In 2017, McKinsey conducted a study on productivity gains driven by technology transformations, such as the steam engine, early robotic technology and advances in information technology. McKinsey sees manufacturing on the brink of the next generation of industrial automation revolution with unprecedented annual productivity growth of between 0.8 – 1.4% in the decades ahead. Advances in robotics, artificial intelligence and machine learning will match or outperform humans in a range of work activities involving fast, precise, repetitive action and cognitive capabilities. To remain competitive, complex industries need to deploy industrial automation more than ever, as intense global competition drives process industries to increase efficiency through reduced operating costs, increased production, higher quality and lower inventories. The highest priority should be to eliminate production losses caused by unplanned downtime and address a $20 billion a year problem for the process industries.


Unsupervised Machine Learning: The Path to Industry 4.0 for the Coal Industry

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Power plants can deploy these innovative technologies today to more accurately predict the condition of assets and schedule appropriate maintenance to correct equipment problems before failure.


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In this special guest feature, Mike Brooks, Senior Business Consultant at AspenTech, discusses how companies can no longer rely solely on traditional equipment maintenance practices but must also incorporate operational behaviors in deploying data-driven solutions using machine learning. For example, a North American energy company was losing up to a million dollars in repairs and lost revenue from repeat breakdowns of electric submersible pumps. In another case, a leading railway freight firm operating across 23 states in the US used Machine Learning to address perennial locomotive engine failures costing millions in repairs, fines, and lost revenue. Companies can no longer rely solely on traditional maintenance practices but must also incorporate operational behaviors in deploying data-driven solutions.


Improving maintenance outcomes with machine learning

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Mike Brooks proposes the use of machine learning software to improve plant reliability and to reduce unplanned downtime. There is a significant need to carry out failure prevention using data-driven truths instead of guesstimates, evidenced by the fact that a combination of mechanical and process induced breakdowns account for up to 10% of the worldwide $1.4 trillion manufacturing market, according to a 2012 report from The McKinsey Global Institute. While companies have spent millions trying to address this issue and ultimately avoid unplanned downtime, only recently have they been able to address wear and age-based failures. Current techniques are not able to detect problems early enough and lack insight into the reasons behind the seemingly random failures that cause over 80% of unplanned downtime. This is where using machine learning software to cast a'wider net' around machines can capture process induced failures.


Machine Learning is Everywhere, What's the Difference?

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There is a lot of talk these days about analytics and machine learning. It seems that machine learning is everywhere. But not all analytics and machine learning are industrial-strength and few can help the world's largest, most important industrial companies drive best-in-class performance. What does it really take to create a world that doesn't break down? You can start on-line with self-help machine learning, with projects kits from Microsoft in Azure, MatLabs, and others.