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Enabling Industry 4.0 and smart factories with IoT and AI – The Microsoft India Blog

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The confluence of smart sensors and Artificial Intelligence (AI) is set to redefine the industrial world. Experts believe a wave of new technologies is creating the fourth industrial revolution or Industry 4.0. Defined by the trend of large industrial enterprises adopting automation, machine learning (ML) and real-time insights and configuration, Industry 4.0 will make global industrial operations smart and efficient. Industry 4.0 comprises of four key pillars: Industry 4.0 will lead to the creation of Smart Factories with machines that can communicate with each other. Backed by AI and a wealth of insights from raw data, these machines could be empowered to configure production processes and make modifications in real-time to optimize operations.


Big data, the cloud and . . . FANUC and Kuka? The Robot Report - tracking the business of robotics

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FANUC, the world's largest maker of industrial robots, plans to start connecting 400,000 of their installed systems by the end of this year. The goal is to collect data about their operations and, through the use of deep learning, improve performance. Similarly, Kuka is building a deep-learning AI network for their industrial robots. FANUC is now moving forward to connect all its manufacturing robots. The system proactively detects and informs of a potential equipment or process problem before unexpected downtime occurs.


Machines Watching Machines: The Value of AI-based Predictive Maintenance in Reducing Manufacturing Downtime

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According to the World Bank, in 2017 (the latest year data are available) the worldwide manufacturing economy added $13.17T in value to the global GDP. Various sources estimate that anywhere from 4 percent to 20 percent of manufacturing capacity is lost to unplanned downtime (depending on the particular company and industry). Choosing a conservative 5 percent number averaged across all companies and industries means that the $13T number is 5 percent lower than it otherwise might be - an astonishing $693B in global productivity lost to unexpected maintenance issues for manufacturers. Reducing that number even slightly has huge potential benefits to the world's economy. Of course, manufacturers have always worked to minimize equipment failures resulting in unplanned downtime and have developed multiple techniques and processes along the way to mitigate the impact.


Edge Computing: An Overview - DATAVERSITY

@machinelearnbot

Edge Computing (EC) allows data generated by the Internet of Things (IoT) to be processed near its source, rather than sending the data great distances, to data centers or a Cloud. More specifically, Edge Computing uses a network of micro-data stations to process or store the data locally, within a range of 100 square feet. Prior to Edge Computing, it was assumed all data would be sent to the Cloud using a large and stable pipeline between the edge/IoT device and the Cloud.


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.