The Internet of Things (IoT), is all about connecting devices into networks that work together. This follows a shift in design from monolithic machines to segmented ones. In other words, IoT is all about micro processing and splitting larger systems into various smaller ones. By doing this, managing the network, upgrading it, and maintaining it become substantially easier.
In manufacturing contexts, industrial Internet of Things (IIoT) is important for a number of reasons. One of the most important is IoT analytics. Manufacturers need data to understand what the status of their performance is and what’s slowing them down. From there, they can make decisions to improve the efficiency of their business. This process can even be automated by combining IIoT systems with artificial intelligence.
However, it’s important to think carefully about how IIoT technologies apply to your business. Combining technologies together in new and innovative ways is one of the best ways to remain competitive as a business. It’s important to consider the existing manufacturing IoT trends this year in 2022 to get ideas about how exactly these technologies can benefit you.
One of the greatest bottlenecks of IoT technology in manufacturing is bandwidth. How much data can all of these devices in a network exchange at a time? The more data that can be communicated at once, the faster and more efficient these systems will be within alternative setups. Network speed is essential for the success of real-time applications and edge computing. However, if data does not need to be communicated as fast as possible, then extremely high bandwidth may not be needed. It’s important to evaluate the needs of your manufacturing business so that you can choose the best options.
For the most consistent and fastest solutions, wired IIoT connections are the best options for manufacturers looking to connect multiple IoT devices together into a network. These use EtherCAT, Ethernet/IP, and Profinet protocols. USB connections are limited in their speed and range, so typically category cables are used to extend their range. In more distant scenarios, fiberoptic cables can be used to connect industrial facilities miles apart from each other.
Being more mature than wireless technology, wired solutions offer proven reliability and noise immunity. However, the greatest downside of these connections is the physical component. Wiring can take up space, have environmental limitations, and is more time consuming to set up. Wired connections also add the additional costs of cabling.
For versatility and ease of setup, wireless connections are far more effective. Although wireless connections are inherently vulnerable to inconsistency, this can be mitigated depending on the type of wireless connectivity used. There are several technologies in use and on the rise today in 2022 for manufacturing IIoT systems:
Bluetooth Low Energy is a technology that is more efficient than Wi-Fi and Zigbee. It is a great choice for devices that are portable and battery powered. However, less data can be transferred at once due to the energy restrictions. It also is a subject to interference in the 2.4 GHz range both Bluetooth and old-school Wi-Fi networks may use, affecting how the devices work.
Zigbee is an alternative connection type that works best with battery-operated sensors with low data throughput. This relies on nodes that interconnect multiple pathways. A central hub coordinator is needed which increases the complexity of the system.
Wi-Fi has much more potential for machine sensors in factories. With 5 GHz access points, these points can provide high speed connections to devices up to 190 feet away. To provide maximum coverage, multiple beacons can be spread throughout the factory floor. This can be the best way to support IIoT sensors that are cable powered with fixed locations. Since additional communications cabling is not necessary, setup of IIoT sensors that can monitor machinery is simple. High speed Wi-Fi connections are one of the best solutions for industrial Internet of Things applications for factories in 2022.
Breakdowns in manufacturing centers are extremely costly. With predictive maintenance provided by artificial intelligence, organizations can save millions. However, industrial machine learning algorithms cannot function without high quality data about the machinery they are evaluating. Industrial Internet of Things sensors can collect data across a network of machines. This data can then be used to identify which machines need to be preemptively scheduled for maintenance and when.
These sensors can also measure temperature, vibration, and electricity usage of machines to estimate potential future points of failure.
Thanks to Industrial Internet of Things networks, quality assurance monitoring can be done remotely and automatically. This can improve productivity and efficiency of manufacturing businesses greatly. Real-time alerts can be sent to allow for more rapid response to issues like unexpected machine failures and other disruptions. Real-time video connectivity through IIoT devices also supports artificial intelligence efforts like automated visual inspection. This allows for AI to detect defectives and remove them from the assembly line before they can be shipped off. AI-driven visual inspection solutions would not be possible without IoT sensors and cameras to give eyes and ears to the decision-making process.
One of the most interesting trends in the sphere of Industrial Internet of Things technology is edge computing. Many industries and businesses have shifted toward offloading processing from local devices to far-away servers that do data processing for them. While this reduces the amount of processing that the local device like a cell phone or PC has to do, it is costly when it comes to time and bandwidth. The goal of edge computing is the opposite, keep processing as close to the ‘edge’ as possible.
In manufacturing contexts, several devices in the local edge network at a factory can handle the processing without having to send data elsewhere for processing. Not only is this faster and more efficient, but it is also inherently more secure. Since the data never leaves the factory, there is no risk of it being intercepted or recovered by a third party.
Forward-looking industrial companies leverage new opportunities by fusing edge computing and AI into Edge AI. The Edge AI concept allows AI computation to be done near the user at the edge of the IoT network, instead of a cloud. That helps bring real-time intelligence to industrial processes, increase privacy and enhance cybersecurity, at the same time reducing costs and securing persistent improvement of the manufacturing processes.
Location tracking has various applications to manufacturing, and all of them rely on Industrial Internet of Things technologies. While GPS is well known to be extremely effective in most outdoor contexts, indoor positioning systems and areas with GPS interference like dense cities with tall buildings can be more challenging to work with. Outdoor solutions are generally in the domain of logistics, but indoor solutions are within the realm of manufacturing.
Real-time location systems (RTLS) are based on wireless technologies, such as Wi-Fi, BLE beacons, UWB, and RFID. They can help identify where products are on the factory floor which can monitor their progress from beginning to end of the production process. This not only can help verify quality assurance, but it can also provide additional data to support digital twins applications.
You have probably at some point wandered into a dark room, only for the lights to turn on by themselves. It may not seem like it at first, but we have a lot to learn from motion-sensing light switches. Their goal isn’t just to make it easier for us to turn on the lights. Their underlying goal is to make it so that if the room isn’t in use, the lights turn off by themselves. Where this crosses over into the Industrial Internet of Things is the concept of energy optimization. We can optimize lights in our factory, but how can we optimize energy consumption of other devices?
Energy optimization can be achieved through various ways, including paying close attention to temperature control systems, industrial machines, and more. A report by EIA reveals that in 2020, 33% of the total United States energy consumption can be attributed to the manufacturing sector. Not only is energy optimization better for the environment, but it can result in significant cost savings.
By using IIoT energy optimization sensors to monitor the electrical status and usage of devices and machines in a factory, operators can fine tune the process and automatically optimize energy usage by various devices. However, this is only one piece of the puzzle. Creating more eco-friendly manufacturing processes will take much more than just IoT sensors and electrical data processing.
Ultimately, the future of the Industrial Internet of Things depends on a number of factors. Thanks to a chip shortage that began last year and that is still plaguing the market, there is a limitation on the number of devices that businesses can field at more affordable prices. With sanctions against Russia intensifying due to the recent invasion of Ukraine, Bloomberg reports that the shortage is going to get worse.
However, this doesn’t mean that IIoT devices are impossible to acquire and implement, and existing systems can be improved as well. In times of disruption, innovation is extremely important. Although the chip shortage is straining expansion of the market, those who can solve problems with existing hardware and resources will remain competitive throughout the rest of the shortage. These businesses will continue to thrive after the shortage ends as well, resulting in even more gains.