Monitoring data allows you to plan and conduct maintenance more efficiently. Establishing a strategy for regular system maintenance protects your investment in assets and minimizes the costs in case of failure. A poorly maintained data center can cost millions and even deny access to customers, so it makes sense to do maintenance ahead of time.
Make your data center infrastructure more robust by armoring your network and doing capacity planning for the inevitable power outages and other problems that are sure to arise. If you have done appropriate system maintenance, then you can then have adequate strategy in place for handling these difficulties more effectively, too. Your hardware and software will be in better shape, enabling them to recover with less friction.
But even during normal operations, in the absence of mission-critical failures, system maintenance offers compelling advantages. A well-maintained system runs more smoothly, drawing less power. This drops expenses, while upping reliability. Having a smart maintenance strategy for your data centers decreases your costs while increasing your uptime.
After human error, problematic maintenance ranks as the main source of data center downtime. While visual investigations and corrective actions are useful, in the modern data center these should serve as complements to more automatic techniques. Superior maintenance speeds up your edge computing and other data center systems.
System maintenance also contributes by reducing the environmental tolls of data centers. Less power use also lessens the use of resources to generate that power. System maintenance is win-win. It is clear then that having an effective maintenance strategy is an all-around benefit. But how can you structure your maintenance to prove most effective for you? Of course, there’s rudimentary reactive maintenance, but that simply isn’t efficient, designed more for mitigating fallout rather than preventing problems. Most proactive strategies fall into one of three buckets: predictive, preventive, and conditions-based maintenance.
Taking a predictive maintenance strategy, as the name implies, involves knowing what problems may occur before they do occur. You assign repair work to assets on the basis of how much strain they can handle. This requires you to assess the condition of resources accurately and non-invasively, which can pose a challenge. But there are a number of techniques you can use to overcome this challenge.
For example, sensors that monitor device conditions can alert admins to issues right as they occur. These sensors can also describe the status of assets during routine operations. The Internet of Things (IoT) complements this functionality, with devices themselves offering dynamic information. They interact with other systems automatically, and predictive data models then process the massive volumes of information to determine when a failure is likely.
Predictive data models combine data from various sources—both those specific to your data center as well as more general data sources—to assemble an initial expectation of failure times. Then, while analyzing ongoing operations, the model adjusts itself in response to real-time data. When necessary, the model will issue an alert, resulting in relevant maintenance tasks.
It is relatively easy for organizations who want to take a predictive maintenance strategy to install monitoring devices on their existing infrastructure. Available tools can measure electrical, mechanical, chemical, and other processes. Each firm can select sensors appropriate to its needs.
Predictive monitoring gives you the inside scoop on your assets, without even having any downtime. This obviates the need for disassembling parts to inspect them. Additional innovative techniques like radiation analysis and ultrasonic analysis allow you to quickly and easily see through equipment to assess any potential internal problems.
IoT technology consolidates the resultant sensor data into a network system that crunches the data. A predictive model assesses the various interconnected parts, gauging when and where a failure is likely to occur. There isn’t a single predictive model; rather, as one collects increasing amounts of information, a model refines itself to become smarter.
Predictive modeling gives your equipment a feel for its own conditions, becoming increasingly accurate. Eventually, your system should have an extremely precise sense of which failures will likely occur and when. These models work by detecting gradual deviations of equipment metrics from normal. Thus, the system automatically learns to find issues before failure.
While predictive maintenance is a highly efficient mechanism for decreasing unexpected downtime, it does require some additional upfront time, money, and effort to install sensors and models. The initial investment puts some organizations off from using predictive maintenance systems. Nonetheless, this approach quickly covers its own costs over time, so it is becoming increasingly popular.
You can start with a smaller predictive maintenance installation, then expand it. Determine which assets merit the most immediate protection—the most critical and expensive resources. You can then add information on these assets’ performance, such as maintenance charts or technicians’ knowledge, to a database. Identifying the failure modes tells you which sensors to install. Working with sensor data, experts can then produce predictive models to start testing.
An hour of downtime can cost over a hundred thousand dollars for an average business—though that’s been known to skyrocket even higher to over a million dollars for some larger businesses. Even mature, established firms suffer from over fifty hours of downtime per year. Predictive maintenance allows firms to slash their downtime by noticing problems before they occur.
In addition to cost savings, predictive maintenance bolsters a business’s reputation. Just one minute of downtime can cost customer patronage for years. Then there is the psychological advantage of having less anxiety over equipment failures. Predictive methods offers additional advantages for data centers, given their technically adept personnel and high costs for downtime.
Predictive maintenance flips old attitudes of fixing things only after problems have occurred. Given how many critical transactions now occur over the internet, including bank transactions, the need for predictive maintenance is clear.
Practicing preventive maintenance as a strategy dovetails quite nicely with predictive maintenance techniques. Regularly assess your hardware and software to evade the need for later reactive maintenance. This can prevent conflicting application versions, faulty air filters, or other problems from ruining your uptime.
To enact a preventive maintenance plan, you want to delineate exactly which resources to maintain, with concrete goals. For example, you may want to plan regular updates of core software applications and their servers. This offers users new features in addition to enhancing security. Establishing a plan beforehand makes it straightforward to measure results.
Each plan for preventive maintenance should include routines in support of the specific organization’s needs. Keep equipment manuals describing the parts’ requirements on hand. A maintenance schedule can offer friendly reminders to encourage adherence to your plan. In the event that you do have to take down a server for maintenance, planning ahead lets you do it when the fewest people possible are using the server.
Doing maintenance before a catastrophic failure occurs can drastically cut costs in such an eventuality. Preventive activities include regularly cleaning the batteries in power backups, vetting cables, and the like. Also, documenting your procedures can assist the maintenance itself, and recovery from any failures.
For preventive maintenance, as with predictive maintenance or any other method, it can help to have external experts on hand. Furthermore, a computerized maintenance management system (CMMS) can ease your job. These programs manage data on procedures, schedules, and other critical aspects of maintenance.
Assigning maintenance tasks to specific employees can streamline the process. Also, assigning priority to the most critical resources allows for mini-maintenance routines when time is constrained.
Some areas to test during preventive maintenance include data storage integrity, event logs, airflow through servers, and software updates, including malware definitions and firmware versions. Also, make sure electrical equipment is working correctly, as well as networking and fire safety equipment.
Preventive maintenance isn’t just a series of tasks: it’s also an attitude. When employees go to work thinking in terms of safety and security, you can have more confidence in things going according to plan. The sooner you start implementing your maintenance strategy, the better your outcome.
Your network equipment costs a considerable sum to purchase, and even a small maintenance failure in your equipment can result in serious service outages. Preventive maintenance can help take unexpected problems out of the equation.
Condition-based maintenance provides an alternative to predictive and preventive strategy. A condition-based approach involves detecting various ambient conditions, including humidity, noise levels, and carbon monoxide and carbon dioxide levels. Then an implemented system advises administrators about maintenance needs.
Condition-based maintenance allows one to replace or repair parts before they reach a failure threshold. The market for condition-based monitoring is expected to reach $3.6 billion in 2026, increasing at a compound annual rate of 7.1%.
We can see a range of maintenance methods from reactive at the most basic, through preventive, condition-based, then predictive at the most complex. As methods increase in complexity, they also increase in cost and performance.
But while condition-based and predictive maintenance have substantial overlap in theory, they differ in that condition-based maintenance does not need the convoluted models of predictive maintenance. Instead, in condition-based maintenance, you make repair decisions on the basis of assets’ present or expected conditions, hence the name. This aims for the least total cost of maintenance.
This style of maintenance strategy hinges on three methodological approaches: data, models, and knowledge. The data method involves calculating expected failure times. Modeling adds more elevated processing, incorporating information on the broader system profile. The knowledge approach goes a step beyond that even, also considering human awareness of the system’s properties.
Any of these three approaches to condition-based maintenance can offer advance warning of potential problems. Thus, these methods assist businesses in doing maintenance at the right time, to save money. They work by reporting the time from potential failure to functional failure to staff, who can then fix problems before the problems detonate.
While the specific workflow for condition-based maintenance can vary somewhat, it generally involves collecting the relevant information (input), processing the data (analysis), then doing the necessary repairs (output). An organization can measure the default operating conditions, measure ongoing developments, notice any highly unusual conditions, then remedy the situation. This responsive process can involve complex tools, or just straightforward visual inspection.
Some of the detection techniques within condition-based maintenance include corrosion monitoring and process parameter trending. These and other methods uncover trouble in early stages, before the deterioration becomes too pressing.
Using condition-based maintenance, you can find leaky pipes before they make a mess of your data center, or replace the parts in machines when they need it rather than on a fixed schedule. This can save you time while keeping your amenities in top shape. It extends the useful life of your equipment, keeping resources operating at optimal levels while decreasing long-term costs.
The earlier you work on maintenance, the better your odds of avoiding mishaps. Cost to repair increases over time, as does the risk of serious failure. Condition-based maintenance can give you months instead of days or weeks to repair problems, which can spell the difference between failure and success.
RF Code Provides Key Data For Your Maintenance Strategy
All of the aforementioned methods for ahead-of-time maintenance require monitoring information. RF Code environmental monitoring and asset management for core data centers can assist you in collecting this data. With RF Code, you can continuously track your valuable assets as well as their ambient conditions—watch your data center as if you were there yourself. This solution includes the complete toolkit that you need to identify and resolve any issues as fast as possible. RF Code provides the data necessary to undergird any robust systems maintenance strategy.
Acquire mission-critical monitoring of your racks and other assets to run maintenance proactively. This maintenance strategy for data centers can save you money and time, while making audits a snap. Ready to protect your assets? Schedule a demo of RF Code.