“AI has the ability to independently and intelligently manage itself and its applications. In the past dozen years of technological breakthroughs, few have come close to the level of impact that AI combined with the Industrial Internet of Things has had on the industrial sector. According to Deloitte’s statistical survey and forecast, the application prospect of artificial intelligence in China’s manufacturing industry is very broad.
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Perhaps this is an important reason why it is difficult for businesses to survive the massive digital transformation brought about by Industry 4.0 without the critical assistance of the IIoT. The combination of artificial intelligence (AI) and IIoT technologies can effectively manage and make full use of the massive data generated in the digital production process, taking industrial process control to a whole new level.
4 Must-Have Capabilities for IIoT Data Management
With the penetration of the digital wave in the industrial field, big data has become the entrance of industrial digitalization. According to IDC statistics, the global data volume in 2019 reached 42ZB, and it is expected to reach 163ZB in 2022, with a compound annual growth rate of 57%. The application scenarios of industrial data in the industrial field are also increasing. Statistics from CCID think tank show that China’s industrial big data market is about 14.69 billion yuan in 2019, and it is expected to maintain a high growth rate of more than 30% in the future. That said, when companies start to deploy IIoT in their industrial systems, the first challenge they face is how to retrieve data from the IIoT system and make it available for real-time analysis and decision-making in the manufacturing process. To ensure that the data management solution can meet the requirements of the IIoT, the following 4 functions need to be focused on:
・ Versatile connection capability to handle various data. The IoT system has various standards, and the data generated needs to comply with various protocols, such as MQTT, OPC, AMQP, etc. Additionally, most IoT data exists in semi-structured or unstructured formats. Therefore, the data management system must be able to connect to all systems and adhere to various protocols in order to be able to receive data from these systems. At the same time, the solution also supports structured and unstructured data.
・ Rich edge processing capabilities. A good data management solution should be able to filter out erroneous records from the system, and it should also be able to enrich the data with metadata such as timestamps or static text to support better data analysis.
・ Big data processing and machine learning capabilities. Due to the sheer volume of IoT data, the system must maintain ultra-low latency when performing real-time data analysis so that the data can be processed in real-time.
• Real-time monitoring capability. The acquisition and processing of IoT data is a continuous process, therefore, a data management solution should provide real-time monitoring through visualization to show the status of the process in terms of performance and throughput at any time.
How is AI impacting the Industrial Internet of Things?
Before discussing this topic, let’s take a look at what expert-level research institutions have to say about the prospects of artificial intelligence and the Internet of Things: According to Markets & Markets, by 2025, artificial intelligence will become a $190 billion worth of technology. industry. IDC believes that 40% of digital transformation initiatives in 2019 are driven by artificial intelligence. Business Insider predicts that there will be more than 64 billion IoT devices by 2025, up from around 10 billion in 2018. Therefore, McKinsey estimates that by 2025, the Internet of Things has the potential to generate an economic value of 4 trillion to 11 trillion US dollars.
From the above data, we can see that artificial intelligence and the Internet of Things, two technological concepts that have existed for decades, are reemerging at the right time and place. They are breaking traditional industry norms and will spark a digital revolution that will transform the 18th century The traditional industrial revolution brought into the 21st century Industry 4.0. With the integration of artificial intelligence, the performance of the Industrial Internet of Things will be greatly improved.
Artificial intelligence is becoming the brain of industrial intelligence
After the basic elements such as data, algorithms, and computing power have been fully developed, artificial intelligence has the foundation for its realization. At the same time, the development of artificial intelligence has also brought good opportunities for the development of the manufacturing industry, and has comprehensively improved the level of industrial manufacturing from multiple dimensions. At present, artificial intelligence has been applied in many application scenarios in the industrial field, such as industrial visual inspection in intelligent production scenarios and predictive maintenance in the field of equipment management.
During predictive maintenance, using existing data, AI algorithms can determine when to implement preventive measures before a machine needs repairs. Computer vision for visual inspection is also a key technology that can reduce costs and increase efficiency. When provided with the right training data and hardware, machine learning (ML) algorithms are more accurate and effective than humans in visual inspection. For example, BMW Companies are already using this technology to ensure the quality control of their auto parts.
Globally, manufacturing companies are increasingly focusing on improving the efficiency of machinery and systems and reducing production costs. With advances in semiconductor technology and the proliferation of affordable sensors and processors, IIoT adoption will continue to increase. According to an analysis by Grand View Research, in 2020, the global IIoT market size will be approximately US$216.13 billion.
Now, the industrial sector is accelerating the move towards smart and autonomous industrial processes, with data collection from IoT devices on an unprecedented scale. When big data, artificial intelligence and IoT come together, a range of opportunities are created for advanced IoT data analytics solutions. In this process, artificial intelligence, especially deep/machine learning technology, provides strong support for the management and analysis of massive sensory data.
A report by research firm MobiDev predicts that artificial intelligence and the Internet of Things will be worth more than $26 billion by 2025. They also demonstrated that AI increased the efficiency of IoT data by 25% and increased industry analytics capabilities by 42%, both at the center of the IoT and at the edge of the network, where AI played an important role. For example, on the assembly line of a factory, the use of artificial intelligence visual inspection for quality control can effectively reduce the manufacturing defect rate in the manufacturing process.
AI + IIoT solutions
Affected by many favorable factors such as advances in semiconductor and Electronic device technology, increased usage of cloud computing platforms, IPv6 standardization, and support from governments for IIoT-related R&D activities, the IIoT solutions and markets incorporating artificial intelligence are growing rapidly. According to the latest market research report from Markets & Markets, the global IIoT market size is expected to grow from $76.7 billion in 2021 to $106.1 billion in 2026, and the revenue of artificial intelligence in this field is expected to reach $16.7 billion by 2026.
Under this general trend, major technology manufacturers have also made great efforts to promote the implementation of AI + IIoT solutions with innovative technologies and products.
TI’s single-chip embedded robot solution provides a decentralized AI model AI+IIoT solution
Robotic automation has been a revolutionary technology in manufacturing for some time, but in the next few years, the integration of AI into robots is expected to transform the industry. Research by consulting firm Accenture shows that by 2035, AI will double the annual economic growth rate, increase labor productivity by as much as 40 percent, and create a new hybrid relationship between humans and machines that will lead to jobs change in nature.
Figure 1: The TIDEP-01006 reference design demonstrates the capabilities of an embedded robot, in which point cloud data from mmWave radar sensing is processed by Sitara AM57x processors (Credit: TI)
With advances in sensor technology, coupled with embedded systems that can fuse these sensor data together, today’s robots have increasingly superior perception and awareness. In TI’s embedded robotics reference design, sensor data must be able to be fused in real-time during inference for AI to function as it should in the system. As a result, designers need to put ML and deep learning models at the edge and deploy inference into embedded systems.
To this end, TI offers a range of embedded processing products, including edge AI-enabled devices for localized decision-making, machine learning, and easy-to-deploy real-time networks. Among them, the Sitara AM57x processor is a model processor for running AI at the edge:
These processors integrate many necessary components for the entire embedded application in the form of a single chip SoC, including functions such as Display, graphics, video acceleration and industrial networking, and can also connect functions such as video, time-of-flight (ToF), laser Several sensors such as radar (LIDAR) and millimeter wave (mmWave), and some also include specialized hardware in the form of C66x digital signal processor cores and embedded vision engine subsystems designed to accelerate AI algorithms and deep learning inference.
The TIDEP-01006 reference design with a Sitara AM57x processor is an autonomous robot design model. In this system, the point cloud data in the millimeter wave radar sensing process is processed by the Sitara AM57x processor. The Sitara AM57x processor is powered by the Robot Operating System (ROS) and is the main processor for overall system control.
In addition, the solution also supports millimeter-wave sensors with IWR6843 packaged antennas, which can reduce design and manufacturing costs, simplify system design, make the sensor size smaller, and shorten the time-to-market. It provides a great advantage for the design of fully autonomous robots with AI attributes Good entry point.
Renesas Electronics RZ/V2M enables low power consumption and real-time AI processing AI+IIoT solution
In applications such as industrial and infrastructure surveillance cameras, the need for real-time, AI-based portrait and object recognition is growing rapidly. Renesas Electronics RZ/V series microprocessor (MPU) is equipped with its unique image processing AI accelerator – DRP-AI (DRP: Dynamically Configurable Processor), RZ/V2M is the first product of this series, it can Real-time AI inference with ultra-low power consumption in embedded devices.
In addition, the RZ/V2M integrates an Image Signal Processor (ISP) capable of processing 4K pixel images at 30 frames per second. RZ/V2M greatly expands the scope of AI in embedded devices, where robots can work safely with humans in smart factories.
Figure 2: Renesas RZ/V2M enables real-time AI inference with ultra-low power consumption in embedded devices (Source: Renesas)
Epilogue
AI has the ability to independently and intelligently manage itself and its applications. In the past dozen years of technological breakthroughs, few have come close to the level of impact that AI combined with the Industrial Internet of Things has had on the industrial sector. According to Deloitte’s statistical survey and forecast, the application prospect of artificial intelligence in China’s manufacturing industry is very broad.
By integrating artificial intelligence algorithms into the IIoT infrastructure, the entire machinery and equipment can be trained and automated, enabling intelligent management and operation of factories. Maybe we can’t see a wide range of AI+IIoT applications yet. I believe that in a few years, the application of artificial intelligence and the Internet of Things in the industrial field will become more and more common.
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