Treon Blog
In the world of wireless battery-operated sensors, various factors influence sensor performance, with battery life being an important factor. While typical IoT sensors offer a lifespan of multiple years, it’s essential to understand the factors that impact battery longevity. But what are these variables, and how can we predict and optimize the battery life of wireless sensors? In this blog, we will delve into the different variables that affect the battery life of the Treon Industrial Node 6. We’ll also explain how we assist our partners in finding the sweet spot between battery life and performance, empowering them to create customized services while meeting their battery life targets.
Ambient temperature
Treon Industrial Node 6 is designed to operate effectively in a broad temperature range, spanning from -40°C to +60°C in hazardous environments, and up to +85°C in non-hazardous environments. However, it’s important to note that both the ambient temperature where the sensors are installed, and the surface mounting temperature, have a direct impact on battery life. The principle is straightforward: higher temperatures result in shorter battery life. In hotter conditions, the device’s electronics consume more power. At the same time, the available capacity from the battery decreases.
For instance, consider this: if you achieve a battery life of over five years at an average ambient temperature of 25°C, the same usage profile at +70°C could cut the battery life in half. This highlights the significance of considering ambient temperature when optimizing the performance and longevity of Treon sensors.
Update frequency
Treon partners often have varying requirements regarding the frequency at which they receive vibration and temperature data from sensors. It’s essential to note that more frequent updates result in higher power consumption due increased use of wireless radio. This increased energy usage accelerates battery depletion, ultimately reducing the sensor’s overall battery lifespan. However, it’s not just about data collection frequency; data processing is also a crucial factor worth considering.
Data processing
When considering the kind of data our partners need, several factors come into play. Are they interested in acceleration or velocity-based key performance indicators (KPIs)? Do they intend to filter data to specific frequency range or perform multiple FFT calculations with linear averaging? Is data decimation necessary or not? With Treon sensors offering a wide range of edge processing capabilities and full configurability for our partners, power consumption can vary significantly depending on how much signal processing and calculations are done within the sensor. The complexity of the processing also matters; the more complex it is, the longer the active periods for sensors microprocessor are. In short, the more processor usage, the more energy is consumed.
Still, it’s often smarter to process data in the sensor itself and send calculated KPIs or FFTs rather than transmit large volumes of waveform data to the backend.
Sending data
When it comes to transmitting data, the frequency and type of data sent both factor into power consumption. For example, sending key performance indicators (KPIs), which constitute a minimal amount of data, has a much lesser impact on power consumption compared to sending large quantity of waveform data. Interestingly, waveform data possesses unique characteristics: it consumes minimal power for processing within the sensor but demands considerably more energy when transmitted wirelessly. In fact, the size of waveform data can be up to 1000 times larger than KPI data.
Amount of measurement samples
The length of the vibration measurement sample has a significant impact to power consumption; affecting not only the size of data transmission when sending a waveform but also the energy needed for on-device processing.
For instance, if you reduce the sample amount used for signal processing and calculations to 50% of the maximum, you’ll notice a significant reduction in power consumption. This simple adjustment can have a substantial impact, potentially extending your device’s battery life by up to a year.
It’s a simple equation: Processing or sending more samples translates to a higher battery consumption. Therefore, optimizing sample size can be a powerful strategy for conserving energy and maximizing the operational lifespan of your device.
Routing
In a mesh network, wireless devices collaborate to extend coverage and enhance reliability by sharing data through interconnected sensors. Each sensor can measure and transmit data, while also serving as a relay point for data from other sensors to reach Treon Gateway. However, it’s important to note that routing data through sensors consumes power, although not significantly in small networks or when dealing with modest data volumes, such as KPIs.
Working with very large mesh networks, particularly when transmitting substantial amounts of waveform data can be more complex. In such scenarios, some sensors positioned at the end of long routing chains may end up routing excessive amounts of data, resulting in a noticeable 10-20% reduction in their battery life.
Fortunately, there are effective solutions to address this issue. One option is to add extra gateways to the network. This allows the network to autonomously reorganize itself, optimizing routing paths for sensors and ensuring a more balanced distribution of sensors to multiple gateways. Another approach is to introduce dedicated sensors exclusively for routing, separate from the data measurement responsibilities.
Battery life: How to determine when battery is low
Treon partners often ask how to recognize when a sensor’s battery is running low. In response, we offer two approaches. The first involves monitoring the battery voltage, which will decrease as the battery depletes. However, due battery chemistry, the voltage drop occurs only after the battery has been significantly depleted. Relying solely on voltage may result in late warning of low battery.
The second approach involves an innovative battery life algorithm integrated into Treon Industrial Nodes. The algorithm continually tracks device usage and ambient temperature. It estimates the remaining battery life and issues alerts to customers accordingly. With this method, Treon partners receive battery alerts with ample time – typically a few months before the sensor requires replacement. This proactive notification empowers our partners to manage their devices more effectively.
Overall, the various factors discussed – update frequency, data processing, and sample amount – are variables that Treon partners can directly impact. Our partners have the power to choose measurement sample amount, signal processing, and calculations, thereby strongly influencing their sensors’ battery life for analytics and services. This emphasizes the collaborative partnership between us, Treon, and our partners. By adjusting the configuration to our partners’ needs, we collaborate to strike a balance between battery optimization and data delivery. This partnerships showcases our commitment to technical collaboration, aiding our partners in building their services according to their preferences.
Read more about the solution and products:
–> Discover how Treon’s expertise in wireless meets B&K Vibro’s innovative approach to predictive maintenance
–> Visit Treon products page to learn more about Treon’s sensors
–> Interested in the Treon Aito Platform, read more at: https://treon.fi/treon-aito-platform/
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