The Emergence of New Delivery Models for Condition Monitoring in the Power Industry

The power generation sector has traditionally been one of the most enthusiastic and proactive users of condition monitoring technology to ensure machine health. This encompasses not only the primary generating equipment – typically steam, gas, wind, and hydro turbines – but also ancillary equipment or so called “balance of plant”. While the primary equipment has employed continuous, online systems since at least the 1970s, the balance of plant equipment has historically been addressed through either calendar-based preventive maintenance strategies, run-to-failure strategies, or – beginning in the mid-1980s – route-based portable data collection strategies.

Most users in the power generation sector elected a “conventional” delivery model where they were responsible for purchasing all necessary instrumentation and computing infrastructure to collect machinery data, and then using that data themselves to detect and correct machinery issues. More recently, new delivery models have emerged – primarily outside of the power generation sector – whereby machine health is delivered as a service in a so-called “outcome-based” model that places responsibility for infrastructure costs, data collection, and data interpretation on the shoulders of a service provider. The service is provided under a monthly subscription and the user is responsible only for responding to identified machinery issues rather than looking for machinery issues via in-house condition monitoring. This new model holds tremendous promise for the power generation sector, having proven itself in numerous industries including food & beverage, pharmaceuticals, refining, automotive, forest products, and more.

In this webinar, Bently Nevada’s Madeline Spencer and Jesse Hanna explore the problems inherent in the various historically popular delivery models and how today’s leading-edge practitioners are overcoming these problems with a rightsized combination of both in-house teams and subscription based services to find a new equilibrium point for optimal value.