Synergy Energy Resources

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Data-Capture

The IIoT is enabled by technologies such as cybersecurity, cloud computing, edge computing, mobile technologies, machine-to-machine, 3D printing, advanced robotics, big data, internet of things, RFID technology, and cognitive computing.[2][3] Five of the most important ones are described below:

  • Cyber-physical systems (CPS): the basic technology platform for IoT and IIoT and therefore the main enabler to connect physical machines that were previously disconnected. CPS integrates the dynamics of the physical process with those of software and communication, providing abstractions and modeling, design, and analysis techniques for integrated the whole.[1]
  • Cloud computing: With cloud computing IT services can be delivered in which resources are retrieved from the Internet as opposed to direct connection to a server. Files can be kept on cloud-based storage systems rather than on local storage devices.[4]
  • Edge computing: A distributed computing paradigm which brings computer data storage closer to the location where it is needed.[5] In contrast to cloud computing, edge computing refers to decentralized data processing at the edge of the network.[6] The industrial internet requires more of an edge-plus-cloud architecture rather than one based on purely centralized cloud; in order to transform productivity, products and services in the industrial world.[3]
  • Big data analytics: Big data analytics is the process of examining large and varied data sets, or big data.[7]
  • Artificial intelligence and machine learning: Artificial intelligence (AI) is a field within computer science in which intelligent machines are created that work and react like humans.[8] Machine learning is a core part of AI, allows the software to become more accurate with predicting outcomes without explicitly being programmed.[9]

Architecture

IIoT systems are often conceived as a layered modular architecture of digital technology.[10] The device layer refers to the physical components: CPS, sensors or machines. The network layer consists of physical network buses, cloud computing and communication protocols that aggregate and transport the data to the service layer, which consists of applications that manipulate and combine data into information that can be displayed on the driver dashboard. The top-most stratum of the stack is the content layer or the user interface.[11]

Predictive Analytics

Predictive analytics is an area of statistics that deals with extracting information from data and using it to predict trends and behavior patterns. The enhancement of predictive web analytics calculates statistical probabilities of future events online. Predictive analytics statistical techniques include data modeling, machine learning, AI, deep learning algorithms and data mining.[17] Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs.[18] The core of predictive analytics relies on capturing relationships between explanatory variablesand the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions.

Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, “Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions.”[19] In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization.[citation needed] Furthermore, the converted data can be used for closed-loop product life cycle improvement[20] which is the vision of the Industrial Internet Consortium.

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