Replacing the Engineer’s Ear with Sound Detection
The sounds machines make reveal quite a bit about whether they’re working properly – what’s wrong and what’s not. The first thing an engineer does when a machine doesn’t seem to be operating properly is to listen to it. In the past, the machines themselves had no way of listening to their own sound to determine health, and therefore they had no way to alert the automation staff that something is wrong. That’s changed.
Recently, Augury, a predictive machine diagnostics company, added sound sensors to its range of condition monitoring. The company designed tools that listen to any machine – from a jet engine to a household appliance – and run it through an algorithm to identify what’s wrong and even predict what’ll break next.
In developing sound analysis, Augury hones in on specific types of equipment. “We focus on three types of machines: pumps, fans, and chillers or compressors. Within these three families we can diagnose any of the machines we encounter,” said Saar Yoskovitz, CEO of Augury. “We don’t need to generate an algorithm for this pump versus others. The sounds these machines make have a unique fingerprint and we can detect it across any model. Like a fan belt squeaking. It doesn’t matter whether it’s a Mercedes or a Toyota, a fan belt sounds like a fan belt.”
Ultrasonic and vibration sensors are common in predictive maintenance. The difference with Augury’s sensor processing is that it’s Internet- and algorithm-based. The data is sent to servers where it is compared with previous data collected from that machine, as well as data collected from similar machines. The platform then detects changes. “We rely on a specific model for your specific machine and analyze it during its operational day so we can alert you to changes,” said Yoskovitz.
The analysis is done in real time and the results are returned to the control engineer within seconds. Sound readings are taken from the machines through Internet of Things technology. The readings are put through a mechanical diagnostics layer, and the results are transmitted back to the user through a smart device.
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