Industry 4.0 and data analysis

Industry 4.0 and data analysis

One of the pillars of industry 4.0 is cyber-physical systems, and one of the main functions of this type of system is to transform data from the physical environment to the digital world, and vice versa.

When we think about the industrial environment, we can identify different types of equipment that have this capability, such as intelligent sensors, controllers that can integrate different machines and devices with capability of Internet of Things (IoT and IIoT). Each of these types of equipment can generate an immense amount of data about the functioning of the industrial environment and its execution parameters. In this sense, we can collect data on temperature in a given equipment, number of rotations of a motor, vibration generated by the equipment, energy consumption and so many other types of data end up being neglected.

However, what is perceived is that cyber-physical systems by themselves do not account for what is called the fourth industrial revolution, as this amount of data does not generate any value. According to Gökalp et al (2016), companies need to process this data and transform them into timely and valuable information for decision making and process optimization. To transform this data into information, there are some technologies that can be applied, among them we can list big data analytics and artificial intelligence. Technologies like these make it possible to identify patterns between data and present relationships that are not always obvious to the decision maker. In addition, it is possible to predict results according to information extracted from the industrial park and even from the supply chain. Since the moment data-based information is used to manage the business, it is possible to promote initiatives to generate revenue or reduce costs, thus contributing to the company`s growth.

In this sense, some techniques for transforming data into information emerged, such as: (i) Machine Learning and (ii) Statistics. The Machine Learning technique can be used so that a system can learn how the process works and eventually propose better parameterizations based on what was identified. On the other hand, using some Statistics techniques, it is possible to predict information using historical data, such as the need for maintenance before a machine fails and causes a complex problem. Finally, the real revolution is in the use of these various concepts in an integrated way and that they can make a substantial difference in decision making, impacting the results presented by the industry.

Today, Antara is prepared to act as an information integrator, allowing the data generated by machines, sensors and equipment to be associated with the information contained in Antara, thus providing support for decision-making. In this way, we are connected with industry 4.0 trends, and always considering the particularities and challenges of each customer.

Reference:
Gökalp, Mert & Kayabay, Kerem & Akyol, Mehmet Ali & Eren, P. & Koçyiğit, Altan. (2016). Big Data for Industry 4.0: A Conceptual Framework. 431-434. 10.1109/CSCI.2016.0088.

* Text prepared by SystemHaus Team.

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