Leveraging Data for Condition Monitoring

Blog | Condition Monitoring | Predictive Maintenance

Leveraging Accurate, Actionable Data for Condition Monitoring Success!

Our team recently returned from IR/INFO — the world’s premier infrared thermography conference. The hotel was buzzing with consultants, independent thermographers, and reliability engineers.

Of course, we were there because we’re interested in condition monitoring — and we quickly found a community of attendees who share our obsession with protecting their companies’ critical assets by identifying issues in equipment before they cause unexpected downtime. Naturally, a huge piece of this conversation centers on data quality. Data quality is central to condition monitoring. The core challenge is efficiently and effectively extracting the necessary data from the asset to identify problems and allow for maximized maintenance planning and minimal disruption to your operations.

How does machine data convert into actionable insights?

As IoT technology has transformed condition monitoring, the industry has grappled with growing pains as it works to deliver on its promise: deeper, data-driven insight into machine health. The path to maturity has had its bumps. These have included integration issues with legacy technology, a sometimes-slow path to ROI for the customer relative to the setup cost, cybersecurity concerns in having every asset in a facility connected to the cloud, and a lack of universal communication and data protocols hampering interoperability between different data systems.

Customers can quickly get overwhelmed by the massive output, and even if they are able to organize it, they often lack the data literacy to interpret the output meaningfully. To ensure that data is genuinely useful to the customer, there needs to be a strong procedure in place to capture, organize, and interpret it. Otherwise, it’s easy to drown in the sheer volume of output.

Getting answers from the data

Data accuracy is a problem for condition monitoring because each fault detection technology has limitations. This is why UpTime designed a wireless monitoring solution to include three different systems of fault detection: thermal, vibration, and ultrasonic. While the advantages of the first two are well-known, ultrasound is a trailblazing technique in condition monitoring, allowing for early fault detection, even for slow-speed (i.e., 1RPM and up) equipment. By combining all three of these modes of fault detection, customers can enjoy the advantages of each while protecting against their respective limitations.

As for actionability, UpTime has made it its mission to not only provide data but also offer a team of experts that can help customers analyze and understand that data. Wireless condition monitoring shouldn’t stop with sensors — it has to include support. Good solutions should comprise software that lays out the implications of data in a way that maintenance managers can quickly understand. We tell our customers that every UpTime sensor connects them with a team of experts. With a system like this in place, we’re able to offer every customer high-resolution data on the health of their assets which leads to actionable insights.

Data is not an end, but a means to help customers keep their equipment running and protect their margins from unplanned downtime.

Leveraging Accurate, Actionable Data for Condition Monitoring Success!

How does machine data convert into actionable insights?

Getting answers from the data

See it in action!

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