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How Augury Helps IoT Realize ROI

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How Augury Helps IoT Realize ROI

Read on to see what one company is doing to address persistent concern over the initial investment needed to see any ROI on industrial IoT devices.

· IoT Zone ·
Free Resource

Great speaking with Saar Yoskovitz, CEO of Augury about the company's solution to providing predictive maintenance without the overhead associated with IoT solutions.

In the three years I've been interviewing executives about the current and future state of IOT, there has always been tremendous promise in using IoT devices in industrial environments for predictive maintenance and the reduction of unplanned downtime. However, organizations have been slow to realize the benefits due to the cost of hardware and the lack of trained IT and big data professionals.

Predictive maintenance has been around for 30 years but it's been manually intensive. Field hardware to produce raw vibration data cost millions of dollars.

Over the past five years with the rapid evolution of mobile and sensor technology and AI/ML, sensors are able to provide a tremendous amount of data that provide insights into the health of a machine letting operations and maintenance know a bearing needs to be replaced within the next month to avoid an unplanned shutdown.

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However, sensors can still cost more than one million dollars while connecting a site can cost several hundred thousand. That's why Augury is giving away the hardware and charging for a subscription to the diagnostics. They also make a portable diagnostic tool so a smartphone can be used to diagnose a problem on site.

Primary applications are on pumps, fans, and condensers/chillers across virtually any industry including hospitals, pharmaceuticals, food and beverage, wastewater, and energy. Augury advised one client that a $20,000 coupling on a chiller needed to be fixed. Doing so saved a $180,000 motor overhaul. The problem was diagnosed with a five-minute recording of the machine.

Augury is also working with OEMs to embed diagnostics into equipment so the OEM can begin selling predictive maintenance as a service. Only 30% of an OEM's profit comes from new equipment. 70% comes from parts and service.

Developers can use multiple frameworks. The solution is comprised of an endpoint speaking to a node via Bluetooth where data is then uploaded to the cloud. The node is like a headless service. You treat it the same as a cloud server using Go, Docker, and continuous integration methodologies and frameworks - including Lambda on the edge. Fog computing is bringing the cloud to the edge. Software developers are able to solve real problems for real people without significant CapEx.

Topics:
iot ,big data ,industrial iot

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