Machine Learning Systems Set to Invade Manufacturing Sector
By Ken Briodagh August 13, 2015
The Industrial Internet of Things (IIoT) is becoming par for the course in the manufacturing plants all over the globe. With this growth has come a huge need for analytical software to manage the influx of data.
According to a new report from Frost & Sullivan, “Investing in the Currency of the Future: Big Data for the Manufacturing Domain,” several industries, including the life sciences, automotive, aerospace, food and beverage, hi-tech and manufacturing sectors, could greatly benefit from investment in machine learning and will likely begin implementing such mechanisms in the near future. These sectors already account for 14 percent of global stored data.
“With respect to data storage and integration, cloud-based systems are most feasible for small and medium enterprises with fewer customization requirements and distributed user locations,” said Rahul Vijayaraghavan, Senior Research Analyst, industrial automation and process control, Frost & Sullivan. “Businesses are also turning to hybrid solutions, which enable the storage and integration of specific data in public and private clouds depending on sensitivity and security.”
After data is captured and collated by smart systems throughout the manufacturing chain, it must be evaluated and value derived in the form of actionable intelligence. To do this, smart analytical systems of machine learning are the most effective approach. Proactive improvements in asset uptime and streamlining maintenance activities will lead to better predictive and prescriptive analytics.
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“The current reactive approach recognizes the cause of a failure post a breakdown,” said Vijayaraghavan. “In contrast, applying complex statistical algorithms and machine learning techniques to evaluate historical and real-time sensor data will help end users identify potential equipment malfunctions well in advance.”
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Source: Machine Learning Systems Set to Invade Manufacturing Sector
Via: Google Alert for ML