How Predictive Maintenance Prevents Data Center Outages

Most businesses cannot function optimally without data center services anymore. Off-site physical data centers have mostly replaced privately owned premises-based facilities.

This shift means businesses can invest in a service that securely stores and manages their valuable data. Business owners also have other data center options, including cloud computing and colocation.

Unfortunately, data centers are not immune to technical issues, damage, and natural disasters. When a data center goes down, it can mean catastrophe for the businesses storing data in it. Downtime always means lost revenue and angry customers.

Studies show businesses can lose an average of $100,000 per hour of downtime. For online retail giants like Amazon, downtime costs can reach $200,000+ per minute.

Predictive maintenance is an easy way to prevent these types of losses. Predictive data center maintenance helps prevent major issues that lead to outages.

In the past, data centers relied only on reactive maintenance, which meant equipment was only serviced or replaced after a failure. But the damage was already done. Businesses had already lost money and customers. No amount of reactive maintenance would fix that.

By contrast, predictive maintenance ensures that data centers do not go down (with a few unavoidable exceptions).

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What Is Predictive Maintenance?

Predictive maintenance means exactly what the words say. Technology helps predict when maintenance is necessary. Predictive maintenance strategies involve using real-time data. It also includes analytics reports to predict equipment failure before it happens.

Data centers use AI technology to help predict what equipment needs fixing or replacing. This AI technology includes sensors and monitoring systems implemented throughout the data center. The data is analyzed by software that can pinpoint destructive trends. The software also identifies potential problems before they cause an outage.

For instance, a cooling fan in a server rack vibrates excessively. Predictive maintenance software will detect the increased vibration. Predictive maintenance sensors will also detect this before the fan stops working. This will prevent the server from overheating, leading to potential downtime.

This gives technicians time to plan for a short maintenance window to replace the fan. A Deloitte study even showed that predictive maintenance reduces breakdowns by a massive 70%.

Benefits of Predictive Maintenance

Predictive maintenance in data centers also offers several other benefits.

For instance, by identifying potential problems early enough, technicians can replace faulty equipment on time. This prevents major failures, which could lead to extended downtime and massive losses.

Predictive maintenance prevents overheating. It also highlights damaging conditions in data centers. This means that equipment and components will last longer. Ultimately, this will reduce replacement costs over the long term.

Moreover, predictive maintenance means more reliable data center operations. Since data centers rely on customers, this will boost customer confidence.

The Technology That Makes Predictive Maintenance Work

The technology that makes predictive maintenance possible is nothing short of amazing. Sensors collect data on temperature, vibration, and fan speed. AI software analyzes all sensor data, alerting technicians to pending equipment failure.

Moreover, these software programs use machine learning algorithms. These algorithms continue to learn from existing and historical data. This helps improve predictive accuracy over time.

Furthermore, cloud-based predictive maintenance solutions offer scalability and remote monitoring capabilities. These are must-have options for modern businesses and data centers.

Switching From Reactive to Predictive Maintenance

Data centers should switch from reactive to predictive maintenance. They must also start with an assessment of the data center infrastructure.

The assessment should include a list of critical equipment and take account of existing maintenance processes. It must also highlight areas within the data center where issues are likely to occur.

After the assessment, sensors can be deployed where the most data collection is required. The data should focus on climate conditions and equipment health.

Data centers must use predictive maintenance solutions that work with the existing infrastructure and include data visualization and reporting features. The sensor data must integrate with the predictive maintenance software to ensure ongoing data collection and analysis.

AI Technology Can Facilitate More Sustainable Data Centers

AI is becoming a permanent fixture in data centers. This is a good thing because AI-based predictive maintenance can help improve sustainability. This is especially true regarding reducing energy use and carbon emissions.

Energy efficiency is already a major factor in the data center industry. Data centers account for around 2% of global greenhouse gas emissions, and it is widely expected that this figure will shoot up to 8% in just six years from now.

Industry leaders continuously look for ways to bring innovation to data centers to reduce the ever-increasing energy demand.

AI technology can help data centers identify opportunities to improve energy efficiency. This will not only make daily operations run more smoothly but will also reduce resource wastage.

Additional Factors to Consider When Implementing Predictive Maintenance

Advanced equipment and software form the basis of data center predictive maintenance strategies. But, data centers must also consider other factors when switching from reactive to predictive maintenance.

For instance, predictive maintenance can only work if high-quality and consistent data is collected from the sensors. It is crucial to calibrate these sensors correctly and replace them promptly if they fail.

These sensors include temperature, humidity, airflow, pressure, vibration, and water leak detection. Data sensors also use contact closure sensors that connect to third-party sensors. These sensors detect when a cabinet door is opened.

Also, predictive maintenance sensors are built to generate alerts. Data center staff may become alert-fatigued if these alerts are not prioritized according to potential impact.

This means they will become overwhelmed by the sheer number of alerts and stop responding. The data center should have a standardized process detailing when and how to respond to alerts.

Moreover, predictive maintenance is not just a once-off installation of sensors and software. It is an ongoing process that ensures a data center stays online. Data center technicians must review the predictive maintenance process regularly to ensure the accuracy of the sensors and software.

Predictive Maintenance Is the Key to Data Center Reliability

Predictive maintenance is an indispensable strategy for data centers. It helps them focus on equipment health so that business customers can continue to offer online services. As technology evolves, predictive maintenance solutions will, too. By embracing this technology now, data centers can promise greater reliability to their customers.