Chat with us, powered by LiveChat

Everything humans have created, from carts to buildings to trains, has required and continues to require regular maintenance in order to stay operational. For millennia, this maintenance has either been in reaction to equipment failure (reactive) or part of regular, scheduled checks (preventive). With the dawn of the information age and the Internet of Things (IoT), a third type of maintenance has surfaced that promises to revolutionize the whole concept, especially in relation to data center operations: predictive maintenance.

Reactive maintenance is the simplest type of maintenance, wherein users replace components only once they have failed, making it one of the least effective ways to minimize downtime and overall costs. No routine checks are performed to attempt to detect pending failures. This means that the costs of maintenance are effectively zero until the day the equipment fails. However, when the equipment eventually does fail, there will be unavoidable downtime until it is replaced. There may also be damage to other components, depending on the nature of the failure. The initial costs of enacting such a protocol may be negligible, but the final costs will surely be the largest of all possible maintenance approaches.

Preventive maintenance is the most common approach and involves regular, scheduled checks that verify if the equipment is liable to fail soon. If it is, then it is replaced; otherwise nothing is done. This decreases the risk of failure: a United States government study found 12-18% cost savings could be obtained by employing a preventative approach rather than a reactive approachii; however employing personnel to periodically check each and every piece of equipment is expensive and, unless a problem is detected, the check would be effectively unnecessary. A better strategy is to take the best of both maintenance types with no checks performed unless there was a reasonable level of certainty the equipment is approaching a point of failure.

Predictive maintenance strives to achieve this ideal. In predictive maintenance, sensors continuously monitor the equipment, and computer programs analyze this data to make predictions about when the equipment is likely to fail. When the computer detects an anomaly that indicates an imminent failure, maintenance personnel are dispatched to perform the necessary repairs.  While the investment required to transition to a predictive approach is substantial, longer-term potential savings promise to dwarf the initial investment by a factor of ten. This is the case for the oil and gas industry, according to a study by the strategy consulting firm Roland Berger .

Prospective implementers should also take into account the potential efficiency increases and downtime decreases. A study by the United States government put the production efficiency increases at 20-25% and the downtime decreases at 35-45%. This is in addition to a 70-75% decrease in breakdowns and a 25-30% reduction in maintenance costs . All in all, it is clear that any company able and willing to make the initial investment would be well served.

Until recently, implementing predictive maintenance in data centers would have been impossible, even for companies able and willing to spend the money. Prior to the computing revolution, measuring and processing the amount of data required would not have been possible. There are countless stories of data centers performing manual temperature walkthroughs to attempt to catch any anomalies, but these approaches are too granular to offer any real improvements. With the advent of powerful computers, wireless sensors, and artificial intelligence programs, however, the benefits of predictive maintenance are ready to be reaped by those brave enough to make the initial investment.

Data centers are good candidates to be early adopters of these improvements. As technology companies, being on the cutting edge of technology revolutions makes a lot of sense. The processing power and storage required to interpret data should not be difficult for data centers to provide, and most new data center assets arrive at the site already equipped with sensors. The only thing missing is a real-time monitoring program to read and store the data, and an artificial intelligence to analyze it. With the penalties for downtime being so steep, the benefits to data centers are surely larger than for most other industries.

The information age created the need for data centers. Now, as time goes on, it is providing data centers with not only an immense trove of data to process and store (in the form of billions of devices being monitored by the Internet of Things) but also with a means of keeping costs low and ensuring downtime is minimized. Humanity has been relying on the twin pillars of reactive and preventative maintenance for millennia it’s time to embrace predictive maintenance as the future.

Field Service Management Blog. (September 15). Retrieved December 12, 2017, from

Operations & Maintenance Best Practices (Rep.). (2010, August). Retrieved December 12, 2017, from Federal Energy Management Program website:

Newsletter Subscription

You have Successfully Subscribed!