IoT data basis for AI applications

Artificial Intelligence is finally establishing itself as a must-have for the modern industrial business. With the development of Machine Learning and Data Science, more opportunities open up for enterprises to take the next level.

But all these exciting and promising approaches would be useless without underlying infrastructure and proper ways of data ingestion.

Essence of IIoT

The Industrial Internet of Things (IIoT) connects people, products, and processes and accelerates the digital transformation of businesses around the globe. With Industrial IoT platforms, companies are enabling new methods of communication, monitoring, analysis, and data-driven decision-making. Leading industrial enterprises use Industrial Internet of Things approaches to design, manufacture, and service products to create value and interact with their customers.

The introduction of the IIoT concepts to the existing business processes is usually done in several steps. First and foremost, industrial devices should be equipped with sensors, controllers, and actuators. As a result, management and stakeholders can obtain accurate and objective data on the state of production- at any point in time, based on the collected information. The processed data should be provided to all divisions of the enterprise. This helps employees from different departments to establish better and more efficient communication and make well informed decisions.

This newly acquired information allows the company to prevent equipment breakdowns, unplanned downtime, reduce supply chain management failures and unscheduled maintenance. When processing streams of unstructured data coming from sensors, filtering and interpreting this data are the next priority. Therefore, the presentation of information in a way that is comprehensible and clear to the user is of the highest importance. For this, advanced analytical platforms are used to collect and analyze production process data in real-time.

To sum up, the Industrial Internet of Things helps enterprises to make production facilities more efficient, flexible and inexpensive. IP-enabled wireless devices, including sensors, tablets and smartphones are already in active use in manufacturing. In the upcoming years, we can expect the existing wired sensor networks to be expanded upon and augmented with wireless ones, which will significantly increase the number of enterprise-specific applications of monitoring and control systems. The next stage of optimization of industrial processes is expected to be attributed to an increasing convergence of operational technologies and the best available information.

AI in an industrial context

Usually, people use the buzzword „Artificial Intelligence“ to describe the ways of getting new insights into their processes by the means of Machine Learning and Data Science know-how.

These days, the notion that AI and ML are extremely useful for predictive operational management and advanced analytics-driven decision-making becomes apparent for an increasing number of companies. Various enterprises have achieved positive changes in their performance based on introducing AI approaches to optimize their overall equipment efficiency (OEE). Data processing is one of the areas in which AI can make a significant contribution. Overall, it paves the way for technological innovation in a variety of areas, from optimizing urban transport and enhancing public safety, to improving the delivery of financial services.

In an industrial context, AI is able to conduct long-term analysis, allowing users to identify trends and patterns over periods of time. Sophisticated AI algorithms enable enterprises to perform predictive analytics based on many possible scenarios, simplifying the problem-solving process for users. This ability enables the business to assess and respond to risks in real-time, make operational changes and avoid unplanned downtime, thereby gaining a competitive advantage.

Why AI needs IoT

In discussions about Industry 4.0, the terms „Artificial Intelligence“ and „Industrial Internet of Things“ are often mentioned. Many already use both concepts as synonyms because the two technologies often complement each other. But there is one more concept which is - albeit unfairly - not as popular as the aforementioned ones: Artificial Intelligence of Things (AIoT).

By combining the capabilities of AI and IoT and getting the advantage of collecting, storing, and producing huge amounts of data, AIoT is bringing a true digital transformation of the traditional work processes and decision-making frameworks. Moreover, it can provide real-time analysis and feedback. While IoT systems can only collect and organize data, AIoT systems along with providing an interface for collecting data, helps to analyze all gathered information using AI / ML. AIoT platform is therefore able to detect anomalies, failures, and security threats to systems in real-time and may even be programmed to react.

2 big challenges

Currently, the two biggest challenges of introducing AI approaches to the existing IoT solution for enterprises are:

1. Insufficient knowledge of AI-capabilities, since it is still in the active R&D phase. In order to follow its development, an extensive mathematical background is required, along with an ability to freely experiment with new approaches.
2. High computational demands which result in high costs that continue to grow with the increase of the complexity of the ingested data and the precision of the AI model.

The solution to these problems can be found in the symbiosis between IoT platforms and scalable AI services. Such a platform is capable of both ingesting time-series data from the industrial equipment, as well as analyzing and providing key insights in a fully automated approach.

Usually, the best way to get started is to try things out. That’s why we are starting our series of AI tools with a virtual trial-and-error supporting approach for industrial engineers: we call it sparring.ai – find out more about it here and stay tuned for more to come!

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