Spotted: The self-described "machine health" startup Augury has developed a predictive maintenance platform that uses artificial intelligence to analyse machines for mechanical errors. The idea is that readings and patterns embedded within the noise from motors, compressors, pumps, industrial-scale heaters, etc., can be used to detect a problem. Augury's sensors record the readings and process the vibrations, temperatures and magnetism metrics of the machines, before uploading them to the cloud to be analysed by AI algorithms, which are generated by baseline readings in the cloud backend. The system gradually begins to recognise abnormal sounds and faulty movements and the machines analysed are then compared to similar appliances on the cloud, relieving the need to retrain models. The technology can be scaled up, and the company has expanded from analysing pumps, fans and chillers, to noncritical machines.

Every manufacturer wants to avoid unexpected downtime in the plant, and that starts with proactively monitoring machines to keep them up and running. For that reason, technology suppliers are offering up plenty of diagnostic applications designed for machine maintenance. But many of the offerings available are point solutions that are too narrow in focus, or they are too broad and collect too much of the wrong kind of data, which does not provide a way for technicians to pinpoint the root cause of a potential problem. Augury, a company based in Israel and the U.S., is changing the conversation around machine health by combining advanced sensor technology, artificial intelligence, and reliability expertise to provide accurate and actionable insights for an entire ecosystem of production line assets. According to Augury, every machine has a unique acoustic fingerprint.

Qualcomm Ventures said Thursday that it has invested $8 million in a New York-based Internet of Things startup that helps companies predict when their machines will fail. Augury, founded in 2011, collects data from equipment via advanced sensors and then applies artificial intelligence algorithms to anticipate when they will break down. It saves customers money by flagging the need for maintenance ahead of a problem. Qualcomm Ventures believes the investment will help jumpstart the emergence of wireless connected factories, shipyards and other industrial operations -- all of which are expected to accelerate with the rollout of new 5G networks. In industrial settings, every machine generates data.

But while the industrial macrocosm, measured by various productivity indices, putters along, there are a growing number of success stories emerging from industrial companies embracing IIoT technologies in tandem with machine learning. The startup FogHorn, for instance, helped the Japanese industrial electronics company Daihen eliminate 1,800 hours' worth of manual data entry in a single factory. And a top beverage company saved the equivalent of 1 million cans of beer through predictive maintenance in one fell swoop. The firm installed machine monitoring technology from the firm Augury, which marries wireless vibration, ultrasonic, temperature and magnetic sensors with machine learning to detect machine problems for a range of industrial machines, including those used by breweries. "And we detected severe bearing wear on a filler -- the machine that fills cans with beer," said Saar Yoskovitz, co-founder and chief executive officer at Augury.

Brakes squeal, hard drives crunch, air conditioners rattle, and their owners know it's time for a service call. But some of the most valuable machinery in the world often operates with nobody around to hear the mechanical breakdowns, from the chillers and pumps that drive big-building climate control systems to the massive turbines at hydroelectric power plants. That's why a number of startups are working to train computers to pick up on changes in the sounds, vibrations, heat emissions, and other signals that machines give off as they're working or failing. The hope is that the computers can catch mechanical failures before they happen, saving on repair costs and reducing downtime. "We're developing an expert mechanic's brain that identifies exactly what is happening to a machine by the way that it sounds," says Amnon Shenfeld, founder and CEO of 3DSignals, a startup based in Kfar Saba, Israel, that is using machine learning to train computers to listen to machinery and diagnose problems at facilities like hydroelectric plants and steel mills.

There is nothing more frustrating than taking your car into the mechanic with only the vaguest sense that something is wrong. You know that odd little creak or strange whine is new, but you don't have a clue what it's trying to tell you. You would like to know before it becomes a serious problem, and an expert is going to charge you a bunch of money to find out. What if we could diagnose machines, and keep them healthy, just by listening to the noises they make. That's the premise of Augury, a startup based in New York City.