August 22, 2016 | Written by: Jim Crosskey
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Weather affects your businesses in a number of ways that normally fly under the radar, including its impact on the assets you manage.
Take the example of a single rail car. It may be designed to operate in conditions under 90 degrees Fahrenheit. The likelihood of failure increases depending on how many days it has operated above that threshold. These operating specifications may be established by the manufacturer, and yet businesses still aren’t using them to predict failure and conduct maintenance. Now imagine you are managing thousands of rail cars, and the potential impact of weather on the health of your assets (and your bottom line) becomes clear.
That is why we introduced IBM Maximo Asset Health insights, a new solution that combines data on weather, age, maintenance history and real-time condition-based readings, to help reliability engineers achieve a more accurate view of asset health.
Today, many businesses utilize a time-based approach to their preventive maintenance (which doesn’t factor in weather or asset condition). While these approaches have yielded improvements in reliability, recent studies have found that these practices can be optimized:
- 40% of preventive maintenance costs are spent on assets with negligible effect on uptime1
- 45% of all maintenance efforts are infective2
Understanding weather data
Moving toward a condition-based approach that factors in real-time performance data alongside environmental factors such as weather will help businesses optimize their preventive maintenance programs. Understanding weather readings, both in historical as well as current conditions, can be used to normalize readings, reducing false alarms and helping identify anomalies.
Examples of weather’s impact on assets spans industries and asset types. In the utilities industry, sag is known to be a key indicator in transmission line failure – a factor caused by weather, temperature and humidity, which elongate lines. In the case of a water utility, one of the key factors for the health of a well field pump may be the temperature of that asset. The ability to correlate the temperature conditions with the temperature readings on the pump can eliminate false alert conditions during hot weather, or help identify anomalies even in cold temperatures.
In each of these examples a reliability engineer would be able to use the Maximo Asset Health Insights work center to quickly asses the health of their assets and take appropriate action – reducing wasted maintenance efforts and increasing uptime.
We are still in the early stages of understanding how weather might be used to better assess and predict the health of our assets. But by making this information available, we are confident that it will become increasingly important to maintaining the reliability of our critical infrastructure.
Learn more about Maximo Asset Health Insights, or read more about using the Internet of Things for preventive maintenance.
1 Source: T.A. Cook, Maintenance Efficiency Report 2013, August 2013. http://uk.tacook.com/fileadmin/files/3_Studies/Studies/2013/T.A._Cook_Maintenance_Efficiency_Report_2013_En.pdf?tracked=1
2 Source: Oniqua Enterprise Analytics, Reducing the Cost of Preventative Maintenance, http://www.plant-maintenance.com/articles/PMCostReduction.pdf