Factories are using AI to warn them when equipment is reaching the breaking point.
What’s new: Services that monitor machinery to predict imminent failure and provide guidance on necessary upkeep are booming, The Wall Street Journal reported.
How it works: Predictive maintenance systems anticipate breakdowns based on historical and real-time data collected from industrial machinery, enabling maintenance personnel to schedule repairs before they incur costly downtime.
- New York-based Augury developed a system that recognizes sounds made by a variety of gear operating at various levels of distress from brand-new to nearly broken. The company outfits factory machines with wireless audio sensors that transmit data to its cloud-based platform. When the system identifies an issue, it sends a real-time update to the plant’s maintenance team.
- Over 100 U.S. companies use Augury’s service including Frito-Lay, which installed the sensors at four plants, adding 4,000 hours of manufacturing capacity in the past year.
- Senseye, a company based in the Netherlands that was acquired by Siemens AG earlier this year, uses data that machines already collect, including pressure, vibration, and torque measurements, to identify looming issues. The company helped aluminum manufacturer Alcoa to cut unplanned downtime by 20 percent.
Behind the news: Sales of predictive maintenance services stood at around $4 billion in 2020. The global total is expected to reach $18.6 billion by 2027, expanding at a compound annual growth rate of 24.5 percent, according to the research firm Research and Markets.
Why it matters: Supply-chain problems have bedeviled industrial companies since the onset of the Covid-19 pandemic. By predicting when a machine is likely to fail, AI can help them avoid costly outages and enable them to stock up on replacement parts ahead of time.
We’re thinking: Predictive maintenance helps reduce costs on an industrial scale, but could it be adapted for households? Imagine if your washing machine could figure out for itself whether that ominous knocking sound during the spin cycle was just a momentary annoyance or truly worrisome.