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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.


Why predictive maintenance is the next big thing in manufacturing - The Data Scientist

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All of these industries operate machinery, equipment, and vehicles that need periodic maintenance. These machines, equipment, and vehicles can also suffer from failures. Most companies currently operate on a maintenance schedule. In other words, they use recommendations from the manufacturer of the equipment or vehicles they use to schedule maintenance. For example, they might service a vehicle once every six months or after every 50,000 miles.


Increasing Efficiency and Uptime with Predictive Maintenance

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In many manufacturing plants today, monitoring is a highly manual process. FOURDOTONE Teknoloji analyzes data from sensors to enable manufacturers to respond immediately to problems, and predict when machines are likely to fail. Downtime can be expensive, and in a tightly coupled manufacturing line a problem with one machine can have an impact on the entire factory. For many factories, avoiding downtime is a matter of luck rather than science: machine inspections are infrequent, and only capture what's visible to the eye. Data is gathered from the machines and analyzed in the factory, enabling an immediate response to emergencies or imminent problems.


Machines to get better preventative healthcare than humans by 2020 - Internet of Business

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A recent study has revealed that 75 percent of IT and field service decision makers believe that machines will receive better preventative healthcare than human beings by 2020, thanks to increasing use of AI. As Microsoft founder Bill Gates once said: "Treatment without prevention is simply unsustainable." He may have been speaking about medicine, but the sentiment holds true across many other sectors. Technologies such as artificial intelligence [AI], analytics and the use of digital twins are playing a vital role in monitoring industrial machines to prevent critical system failures, detecting issues and scheduling maintenance before outages occur. Given that 10 percent of emergency field service work is predicted to be triggered and scheduled by AI by 2020, a new study from IT market research company Vanson Bourne, conducted on behalf of ServiceMax, has sought to gather industry opinions around increasing automation.


The CEO Of GE Digital On What Is Next For The Industrial Icon

Forbes - Tech

Ruh: Seven years ago when we talked to customers and other companies, they did not understand why we were doing it. Then at the C Suite and the board, there became a recognition about three years ago, and they would ask us what we are doing. Now they are asking us how. I believe the time is now because there is a general acceptance that the future is going to be on the ability to use digital to drive greater productivity as well as the efficiency with your relationship with your end customer. When we go to customers now and we talk about selling them our digital portfolio, it always starts with outcomes.