Predictive models for each fan are based on physics-of-failure models for bearing life and any life-limiting electronics that either have reliability data from the fan manufacturer or have undergone life testing.
Fan life is affected by local air temperature, fan speed and total rotation time. To track local air temperature, the predictive model uses either direct measurements or accurate estimations from one or more air-temperature sensors. For the rotational time, the drive tracks a fan’s on/off state, and the actual fan speed is monitored.
When a fan is not rotating, life isn’t being consumed. A fan-derating parameter also reduces the calculated remaining life. This parameter accounts for other stresses, such as environmental contamination, which reduce fan life.
DC Bus Capacitors
A DC bus capacitor’s predictive model is based on a physics-of-failure model from the manufacturer. Two factors that affect capacitor life are internal temperature and applied voltage. To calculate the heat generated inside the capacitors, the predictive model uses several sensor and control values. The internal temperature is calculated from two temperature sensors and the heat-generation estimates using an empirical model derived from extensive thermal testing.
Contactors and Switches
The lifespan models for a drive’s main circuit breaker, pre-charge contactor and molded case switch all are based on the number of no-load disconnect actions. Each action consumes life from the total available life of these components.
The line-capacitor life in a drive’s LCL filter is most affected by capacitor temperature. The predictive model combines manufacturer data with a physics-of-failure model that uses temperature as an input. Capacitor temperature is calculated from a model that uses data from extensive thermal testing. The component’s life is consumed faster as the ambient temperature increases, and elapsed and remaining life calculations are updated by the minute.
What Does This All Mean?
Predictive maintenance helps reduce unplanned downtime and boost overall productivity. Drives with built-in predictive-maintenance functions are designed to do a lot of the hard work for you. They already have life models that learn and adapt to changes in the application and environment, so all that remains is to develop a maintenance plan for using the insights the drives give via life consumed and predicted remaining lifetime.
The Journal From Rockwell Automation and Our PartnerNetwork™ is published by Putman Media, Inc.