Maintaining continuity and resilience is fundamental in the high-stakes world of data center operations, where uptime directly impacts revenue. Though the servers and IT infrastructure often receive the most attention, diligent maintenance of behind-the-scenes critical assets is essential. Traditionally critical asset maintenance has occurred either through unscheduled, run-to-fail methods or using planned, scheduled maintenance. But with the increasing amount of data and the tools to easily analyze and act on it, the ability to conduct predictive maintenance can mean a huge savings in the cost of labor, spare parts, and equipment.
Unplanned vs. Preventive vs. Predictive Maintenance Defined
Unplanned Maintenance is reactive break/fix work conducted when problems unexpectedly arise, often requiring emergency repairs during crucial operating hours. Also called run-to-failure or reactive maintenance, this is where operators wait until equipment failure to conduct maintenance and repairs, often as a labor and spare part cost cutting measure. While relying on unplanned maintenance may create immediate cost savings, using this method on critical assets runs a high risk of increased downtime and service disruptions, a reduction of an assets lifespan due to neglect, and increased chances of more extensive and costly repairs when an asset fails.
Preventive Maintenance, also known as scheduled or planned maintenance, takes a structured, proactive approach to caring for assets before issues occur. Regularly scheduled preventive maintenance at predefined intervals aims to identify potential problems early and address them preemptively. These schedules are usually based on manufacturer recommendations, industry best practices, or internal maintenance plans. Scheduled upkeep can minimize unplanned downtime, enhance equipment reliability, and increase asset lifespan as issues are addressed proactively. But planned maintenance doesn’t take into account real-time conditions or early signs of equipment failure that predictive maintenance solutions can detect. And schedule maintenance can lead to unnecessary services that prove costly in terms of time and resources — and introduce increased risk in asset reliability. With 70% of outages being caused by human error, the fewer chances someone has to make a mistake the better.
Predictive Maintenance is a proactive approach that utilizes data and technology to predict when equipment is likely to fail, allowing for maintenance activities to be performed when needed but before an asset fails. This condition-based maintenance uses predictive analytics for an efficient allocation of maintenance resources, minimizing downtime and service disruptions, and extending an asset’s lifespan by addressing issues before they can cause failure.
The Critical Benefits of Predictive Maintenance
Smart data centers use predictive maintenance as a balance between maintaining reliability and cost-efficiency by harnessing real-time, clean, curated, and connected data to make maintenance decisions. The advantages include:
- Increased Equipment Uptime: By using predictive maintenance, data centers can address problems proactively, significantly reducing downtime and maintaining high equipment availability, ensuring smooth and uninterrupted operations.
- Cost Savings: Data centers can avoid unnecessary, scheduled maintenance and reduce the expenses associated with emergency repairs, spare parts, and overtime labor.
- Extended Asset Lifespan: Proactively addressing issues and maintaining equipment in optimal condition can extend the lifespan of critical assets, resulting in a longer ROI and reduced capital expenditure on replacements.
- Improved Safety: Data centers can address safety-related issues promptly and in advance, preventing accidents and injuries associated with equipment breakdowns.
- Better Resource Allocation: By performing maintenance tasks when necessary, data centers can reduce idle time and maintenance costs associated with scheduled maintenance that may not be required.
- Data-Driven Decision-Making: A data-driven approach provides valuable insights into the condition of assets, allowing data centers to make informed decisions about maintenance strategies, replacement timing, and operational improvements.
- Customized Maintenance Schedules: Data centers can tailor maintenance schedules to the actual condition of each asset. Maintenance tasks can be performed when data indicates they are needed, rather than relying on fixed intervals, optimizing asset management and performance and reducing operational disruptions.
Creating an Effective Predictive Maintenance Strategy
Creating an effective predictive maintenance program involves several critical steps to ensure the successful implementation and operation of the program. Here are the key steps to follow:
- Select The Right Assets: Not all assets may require predictive maintenance work, so focus on those that have a significant impact on your operations and that can benefit from predictive maintenance.
- Data Collection and Sensors: Install appropriate sensors and data collection systems on the selected assets to gather relevant data. Ensure the data collected is accurate, reliable, and accessible in real-time.
- Integrate Data and Analysis in a Central Operating System: Integrating your operational data from across your portfolio into a single system allows you to standardize your data and ensure it is clean, accurate, and available in real-time. Once there, the right operating system will utilize advanced analytics and artificial intelligence, and machine learning algorithms to analyze your data and metrics to detect patterns, anomalies, and early signs of equipment degradation or failure. In MCIM – the Data Center Operating System – that data is available through clear dashboards that can inform executives and operators on the status of their entire portfolio in real-time.
- Use Asset Reliability Benchmarking to Establish Baselines: Cross referencing your data center’s data with MCIM’s asset reliability benchmarking allows you to see how your assets are performing against global averages. With this information, you can create baseline performance profiles for each asset and create a reference point for deviations and issues.
- Condition Monitoring: Continuously monitor the condition of your assets in real-time. Use alerts and notifications to trigger maintenance actions when anomalies are detected.
- Maintenance Planning: Use the insights from your predictive maintenance system to plan and schedule maintenance activities. Ensure that maintenance actions align with the asset’s actual condition and prioritize tasks based on criticality.
- Spare Parts and Resources: Maintain an inventory of critical spare parts and ensure that maintenance teams have the necessary resources and training to execute the required tasks efficiently.
- Documentation and Reporting: Maintain thorough documentation of maintenance activities, including work orders, task completion, and results. With a Data Center Operating System like MCIM, all of this information can be stored directly in the asset’s records, making them available at any time from anywhere, even in the field via mobile devices.
Key Takeaways for Your Data Center’s Predictive Maintenance Strategy
Mission-critical data center operations leave no room for disruptive unplanned downtime. By implementing a data-driven approach that harnesses real-time sensor data and advanced analytics, data center operators can detect early signs of equipment degradation or impending failures. This proactive strategy allows for timely interventions, reducing the risk of unexpected downtime and costly disruptions to critical IT services.
Powerful platforms like MCIM optimize data center maintenance planning, scheduling, and work order management, improving compliance while eliminating oversights. Prioritizing predictive maintenance is a strategic imperative for any data center seeking to maintain a high level of uptime, meet service level agreements, and stay ahead of potential issues that may impact the facility’s mission-critical operations.