The IoT and AI are separate technology trends that are both making waves in industry. The IoT can connect devices together, giving and receiving signals like a nervous system. In contrast, AI can act as a brain, using data to make informed decisions that control the overall system.
When joined together, the two are capable of delivering intelligent, connecting systems that can self-correct and self-heal themselves – forming the Artificial Intelligence of Things (AIoT). Conventional IoT technologies such as cloud computing and machine-to-machine (M2M) communication have allowed manufacturers to complete three key tasks: connect machines, store data and make it meaningful. Now, as we introduce AIoT, they can benefit from a fourth capability – to act.
However, to make AIoT feasible, manufacturers need a data management system that can support fast decision making. While cloud storage is possible, analyzing data closer to its source – at the edge – takes AIoT to the next level.
To unlock the power of AI, decisions need to be made with as little latency as possible. If the AI system receives an alert that there is a machine fault, or the speed or movement pattern of a machine should be altered for more productive operations, it can act on these insights immediately and halt or alter production.
By integrating the AI system at the edge, instead of the cloud, manufacturers can unlock the value of ultralow latency, allowing machines to be switched off as quickly as possible and fewer products are damaged or defective. The same is applicable for process optimization activities, such as changing the speed or type of movement of a machine. An AI system at the edge can send instructions to equipment to improve its performance faster than from the cloud.
Integrating AI and processing data at the edge offers increased security. Cloud computing can present a n umber of security issues, as the data is stored by a third-party provider away from the company’s premises, and is accessible over the internet. Edge computing can work as a complement to overcome these security concerns by filtering out sensitive information at the source and storing it on-premise, so there is less tr ansfer of confidential material to the cloud.
Another use case where it is advantageous to integrate AIoT at the edge is when visual inspection systems are involved. Cameras and sensors create massive amounts of data, and therefore it makes better sense to analyze and filter this data at the edge, instead of sending it all to the cloud or large centralized system.
In addition, facilities often have a high number of mobile devices connected to the AIoT, and are therefore handling a huge amount of data. Sending all of this data to the cloud may not be possible, so it’s better to conduct the analysis at the edge. Edge analytics can extract the higher-value features from the raw data, sending only the important and necessary information to the cloud, such as remaining machine lifetime, for example.
Integrating the AIoT
In order to integrate AIoT at the edge, industry leaders must first build an AI model offline. They must then train the model using previously stored datasets until it meets requirements before exporting and applying it online with new live data.
However, applying the model to real-time data in an online scenario is very different to testing it on stored data that has already been sorted in the training stage. Real-time data hasn’t been filtered or categorized, and each set may arrive at different times, creating a chaos of information for the AIoT.
Enter edge analytics
To make sense of the data, it must be processed before it can be used by the AIoT. That’s where edge analytics come in. The Crosser Platform can help prepare the data in a number of ways before it reaches the AIoT. For example, it can harmonize data from the wide variety of machines on the factory floor, that might be in different formats as it has travelled in from multiple sources.
Data from different sources and formats is aggregated by the platform at regular intervals. In addition, if the data sources have different sampling rates, then the platform can fill in intermediate values so that the models can be updated with new data from all the sensors in each update. It can also create different types of windows over time series data.
The platform can also be used for feature extraction. Depending on the model being used, additional features may need to be created out of the raw data. This could be, for example, taking vibration data and converting it from the time domain into the frequency domain. All of these steps streamline the data before it reaches the AIoT.
It’s true that machine intelligence holds great power, but other supporting technologies can help uncover its full potential. Industry leaders who integrate the AIoT at the edge can reap the benefits of an efficient and reactive control system – optimizing processes, fast.
For a list of the sources used in this article, please contact the editor.
Johan Jonzon is CMO and Co-Founder of Crosser, a Swedish software company with installations in over 20 different countries. It designs and develops a Low-Code software platform for streaming analytics, automation and integration for any edge, on-premise or cloud. Its aim is to remove complexity, simplify development and to enable non-programmers to innovate faster with a dramatically lower total cost of ownership. Its vision is that there are enormous business opportunities for companies When Machines Talk™.