Wireless Sensor Networking for the IIoT

Figure 1. Analyzing maintenance data to forecast machine maintenance. (Image: Kristian/Adobe Stock) Factories of all sizes are incorporating automation at ever increasing rates. Among the reasons for that are reshoring, the idea that automating factories is a way of lowering labor costs for U.S. manufacturing so that domestic manufacturing becomes more cost-effective when you compare it to the costs of offshoring. You can take advantage of the much lower labor rates in many countries, but you have to add in the costs of more complex management and logistics, as well as the costs of shipping. And then there’s the U.S. labor shortage, the difficulty of finding enough people who are willing to work in factories, while at the same time, a significant portion of the existing workforce is aging toward retirement. And of course, the increasing productivity gains due to automation are good for the bottom line. Automation not only reduces the costs of production in the long run, but it also helps maintain reliable high-quality results and consistently predictable time frames. The downside of automating an existing factory is the initial investment, not just in dollars, but also in the necessary down time for making such basic changes. However, most factories already have some automated processes using PLCs and other industrial controllers, generally running independently of each other, so that’s a head start. The next step is to integrate all of that into a single network — the Industrial Internet of Things (IIoT). Ideally the factory network should also connect with the office network, to enable management to make more informed decisions. And it should enable connecting to the cloud for complicated analytics and large data storage — as well to the internet for connectivity beyond the factory. Why Wireless? To begin with, installing a wireless network is much less expensive. The costs in labor, materials, and downtime, for wiring a factory are far greater than for setting up a wireless system. And once in place, a wireless network is much more flexible. As processes change or new equipment is added, it is relatively simple to add or reprogram sensor nodes. Also, wireless sensors can be installed in locations that would be difficult to reach with cabling, for example on rotating machinery. Designing the System One of the main challenges with setting up wireless sensors, is powering them. Even if you use some sort of power harvesting, you still need power storage, usually with batteries. If the batteries have to be changed often, wireless is a non-starter, so keeping power low is of primary importance. One strategy for keeping sensor power low is to reduce the amount of data transmitted from each sensor because streaming data uses a relatively large amount of power. The trouble is that once an IIoT network has been installed, the maximum benefit comes from obtaining as much data as possible and sending it to a local server or to a cloud data center for analysis. But in general, only a small percentage of the data is relevant. External analytic data-crunching can sort the wheat from the chaff, but the size of the data stream and the amount of computing can overwhelm systems. A solution is to do preprocessing at the “edge” — right at the sensor — to determine what data is significant and only send that. Low Power Edge Processing But for edge processing to provide a net improvement in power reduction, the processing itself has to be done at low power. On that subject, I had a discussion with Nandan Nayampally, Chief Marketing Officer of BrainChip, makers of the Akida IP platform, a neural processor designed to provide ultra-low-power edge AI sensor network preprocessing. It starts with fully digital neuromorphic event-based AI and can learn on the device to trigger outputs only when there is significant information. It has its own memory that it uses to analyze the data, thus avoiding the energy-intensive transfer of data back and forth to utilize remote data storage. It also significantly reduces the amount of bandwidth required for the processing. Figure 2. (Image: BrainChip) According to Nayampally, the series is focused on three general configurations (See Figure 2). The Akida-E is the most basic of the solutions, dealing with sensor inputs like vibration detection, anomaly detection, keyword spotting, and sensor fusion. Akida-S is more mid-range. It can do microcontroller (MCU)-level machine learning for more complex tasks such as presence detection, object classification, and biometric recognition. Finally, on the right-hand side of the figure, the Akida-P […]

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