Intelligent Maintenance: Is machine learning smart enough to help industry?
How access to massive amounts of data benefits machine design, control systems, production, maintenance and business
With the leading technologies of business and industry today, the wealth of big data can overwhelm users and decision makers. Fortunately computers and machine-learning applications can help.
In regards to machine learning algorithms, their top advantage is the ability to process large amounts of data analytics to identify patterns and trends not readily visible using traditional statistical tools. This information can be leveraged for preventive maintenance and system optimization.
The goal is to make sense of all the data in time to make the right operational decision, and machine learning can be very helpful. Machine learning algorithms will be able to work with complex data and make intelligent decisions in real time. The outcome would be improved productivity by reducing scrap, and reduced maintenance cost by moving to predictive maintenance rather than preventive maintenance.
Human intuition is limited, machine learning can point out correlations that are subtle and that humans can’t see. It has been found that performance of the algorithms improve over time - it is often true that a weaker algorithm with more data will ultimately outperform a stronger algorithm with less data.
Benefits include:
More efficiency in the form of reducing downtime and a growing capability to predict failures in order to avoid unplanned downtime events.
Identifying operation speeds that are more cost-effective than simply running at max speed
Discovering which system designs are less vulnerable to breakdowns and efficiency losses.
Advances and Improvements:
The biggest advancements related to machine learning will be centered on data collection. The biggest gains will come when streamlined data collection and conditional monitoring are combined to create ideal operating profiles for processes. The most prevalent current solutions allow for identifying trends in the process that have had a large, negative impact on efficiency.
Article Title: Is machine learning smart enough to help industry?
Publication Date: April 04, 2016
Website Title: Control Design
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