Silicon Labs Provides Predictive Maintenance Solutions to Help Reduce The Downtime

时间： 2021-03-04 来源：Silicon Labs

技术问答

In 2017, ARC Advisory Group estimated the global downtime in manufacturing industry is in the range of one trillion dollars annually. That is a lot of money, and to put it into a perspective, the global GDP in 2019 according to World Bank was 87.8 trillion dollars. It is not surprising that reducing the downtime is one of the most attractive outcomes industrial IoT can provide.

Why does downtime cost so much and how to reduce it?

What options exist in reducing downtime? Predictive maintenance has proven a cost-efficient application to address downtime challenges and provide ROI to justify projects. IoT Analytics forecasts that the predictive maintenance market is growing at 39% CAGR to $23.5 billion dollars in 2024. What makes predictive maintenance so attractive is that it addresses two key issues at the same time. If the machinery or components like motors, pumps and bearings are run until they fail, there can be more costly damages done to the equipment due to the failure. In addition, there is the time spent by the staff trying to get replacement parts on site and then working overtime to fix the issue. All of this adds to the final cost of an unplanned downtime event and contributes to lost production. On the other hand, if the equipment is over-serviced by changing wearing parts too often or too early, the downtime also increases because of the too-frequent scheduled service breaks. In predictive maintenance, the algorithms use sensor data collected from the machinery and components to warn the operator of a future failure condition ahead of time, allowing ample time to schedule and plan for the maintenance before the failure occurs.

Key care-abouts in predictive maintenance

Predictive maintenance solutions commonly rely on detecting anomalies in vibration fingerprints of motors, pumps, bearings, and other devices that run the industrial and commercial processes. Because cabling costs for adding vibration sensors are immensely high, these sensors are typically leveraging wireless communications and powered from a battery. We have some unique advantages for predictive maintenance solution developers. Our products include industry-leading low-power consumption wireless SoCs and modules. Using the built-in low-power modes, the sensors can benefit from fast wakeup times and balancing time between sleep and active modes. This power optimization translates into longer battery life, which means lower total cost of ownership (TCO) for the end customer because the sensors require less maintenance during their lifetime.

Choosing the best-fit wireless technology for your application

The environments in which predictive maintenance solutions are deployed vary to a large degree. This is why the solution developer should partner with a communications expert like we that can support a wide range of wireless technologies in multiple frequency bands. For longer-range needs, technologies such as Wi-SUN, Mioty, or other sub-GHz options are more suitable. Local networks within a factory or a plant could benefit from Bluetooth and mesh technologies, or leverage existing dual-band Wi-Fi infrastructure to connect the sensors.

Embedded AI/ML changing the landscape for predictive maintenance

Artificial intelligence and machine learning (AI//ML) has extended its reach from being a cloud-level application requiring massive computing resources to something that can be efficiently executed at Cortex-M level microcontrollers. SILICON LABS' AI/ML partners have built tools that allow predictive maintenance algorithms to run on just a few kilobytes of RAM memory. The edge pre-processing means that the local radio can be turned off until there is an anomaly that needs to be reported to the back office system and the operator. This can further conserve the precious battery capacity and enhance the TCO.

How to get started?

If you want to take part in solving this trillion-dollar question, a good place to start is by exploring our Thunderboard Sense 2 Evaluation Kit. This kit integrates wireless communications with an array of sensors, including accelerometer and temperature, which are the most common in predictive maintenance applications.

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本文由翊翊所思转载自Silicon Labs，原文标题为:A Trillion-Dollar Question for Industrial IoT，本站所有转载文章系出于传递更多信息之目的，且明确注明来源，不希望被转载的媒体或个人可与我们联系，我们将立即进行删除处理。

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