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Build the program around asset criticality, reliable telemetry, operating baselines, actionable condition indicators, reviewed alerts, and a documented maintenance workflow. AI and machine learning can help detect deviations and recommend action, but they do not replace facilities staff, safe operating procedures, or engineering judgment.
What an AI-driven maintenance program should do
Condition-based maintenance uses evidence about an asset’s condition to identify degradation before it becomes a failure and to inform when maintenance is needed. AI is one possible analytics layer in that process—not the program itself. A useful program connects equipment data to a decision, a safe work process, and feedback about what happened.
ASHRAE’s AI Data Center Energy Performance Framework recommends using real-time sensor data from power and cooling equipment to establish baselines and detect deviations. The U.S. Department of Energy (DOE) describes energy management information systems (EMIS) that can create or exchange work orders with a computerized maintenance management system (CMMS). Together, those ideas form a practical operating loop: observe, assess, review, act, and learn.
That distinction matters: an anomaly score is not a work order, and a prediction is not authorization to change equipment operation. Facilities personnel remain responsible for interpreting results, approving work, and ensuring it is executed safely and in compliance with applicable requirements.
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How to build the program
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Set scope and prioritize critical assets
Start with the facility’s reliability requirements and asset inventory. Prioritize equipment by the consequences of failure, redundancy, maintainability, and the condition data available. Power and cooling are natural starting domains in ASHRAE’s guidance, but there is no universal ranking: the right scope depends on the facility. Do not assume every asset needs a new sensor or its own machine-learning model.
For each selected asset, record its role, operating context, existing monitoring, maintenance history, and the consequence of losing it. This makes it possible to decide where better monitoring can change a maintenance decision, rather than collecting data without a defined use.
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Audit telemetry and maintenance data
Map the data already available from controls, sensors, alarms, equipment-state points, commissioning records, and maintenance systems. Check that the information is usable before building analytics around it:
- Are timestamps aligned, and are gaps or delays visible?
- Are units, asset identifiers, and point names consistent?
- Are sensors calibrated and measuring the intended condition?
- Do readings correspond to the equipment state the analysis is meant to assess?
- Can alarm history and work-order records be associated with the relevant asset and time period?
DOE notes that much installed equipment already has useful instrumentation. Add sensors or integrations when a defined information need is not met by existing systems. Any new instrument should suit the asset’s accuracy and environmental requirements and follow the facility’s approved controls and integration approach.
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Establish and maintain operating baselines
Use initial commissioning and recommissioning to characterize acceptable behavior across relevant loads and operating conditions. Retain trended commissioning data where practical; it provides a reference for distinguishing normal variation from deterioration.
Update baselines after significant equipment upgrades, additions, or operating changes. A stale baseline can flag a normal change as a fault—or absorb gradual deterioration into what the system treats as normal. Involve controls and operations staff in commissioning and procedure validation so the baseline reflects intended operation, not just what a system happened to do during a short observation window.
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Select condition indicators tied to failure mechanisms
Choose measurements that have a plausible relationship to degradation and can inform an action. DOE gives two examples: rising differential pressure across an air-handler filter can indicate loading, while reduced heat transfer across a heat exchanger can indicate a performance problem. These are examples, not universal thresholds; actual interpretation depends on equipment design and operating conditions.
For other assets, choose indicators using the asset’s failure modes and manufacturer and engineering guidance. Define what a change means, what context is needed to interpret it, and who should review it. Avoid generic alert limits that have not been validated against the facility’s equipment and operating data.
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Choose analytics that fit the data and decision
Begin with clear rules or statistical methods where they can answer the operational question. Use machine learning when there is enough representative data and a specific reason to model more complex behavior. DOE describes advanced pattern recognition and machine learning as ways to learn an asset’s operating profile across changing load, ambient, and process conditions.
Configure alerts around meaningful deviations and decision boundaries, not merely unusual values. Before expanding reliance on an alert, evaluate false alarms and missed detections against known operating events and maintenance outcomes. The reviewed official guidance does not prescribe a model architecture or universal probability threshold, so those choices require facility-specific validation.
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Connect alerts to a reviewed work-order path
Define how an alert becomes an operational decision. A practical path is for a condition alert to include the asset, observed indicator, relevant operating context, and supporting trend; a qualified reviewer then assesses it and, where appropriate, creates or updates a work order. Integrate the EMIS and CMMS where the systems support it, and capture the work performed and findings when the job is closed.
Work-order completion feedback helps determine whether an alert was useful, whether the suspected condition was confirmed, and whether it should lead to a change in monitoring or maintenance. It also supports tracking issue resolution, downtime, and time to repair or replace. An alert should not silently trigger a control change or maintenance action outside the facility’s documented approval process.
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Define roles, limits, and safe procedures
Document who reviews alerts, who authorizes work, what operating limits apply, when escalation is required, and how work is performed. Keep facilities staff accountable for approval, safety, compliance, and execution. Periodically review maintenance and operating procedures (MOPs/SOPs) and check that alert handling aligns with control logic and the facility’s approved procedures.
ASHRAE’s framework treats AI/ML as a means to monitor, predict, and recommend—not as a replacement for personnel who make and carry out operational decisions. Test alert responses and failure scenarios with the people responsible for operating the facility before relying on them in live conditions.
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Validate and improve the complete operating loop
Commission the analytics and workflow as part of the facility’s operating environment. Verify that data reaches the right asset record, alerts arrive with enough context, reviewers can take the intended action, and completed work returns useful feedback. Reassess performance after changes to equipment, workload, controls, or operating conditions.
For liquid-cooled systems, ASHRAE’s commissioning guidance specifically emphasizes proper cleaning, flushing, and passivation; insufficient fluid cleanliness or rigor can contribute to fouling or leaks. Confirm the applicable equipment guidance and procedures for the particular installation.
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What to measure—and what not to infer
Use local operating history as the baseline for evaluating maintenance performance. DOE identifies failures, downtime, replacement time, maintenance time, and work-order completion feedback as useful operations and maintenance measures. Track trends in the context of asset coverage and operating changes; the reviewed official sources establish no universal target for failure reduction, model accuracy, or return on investment.
| Measure | What it helps answer |
|---|---|
| Failures and downtime | Are assets failing or spending time unavailable, and how is that changing? |
| Maintenance time and time to repair or replace | How much effort and elapsed time are needed to restore or replace equipment? |
| Work-order completion feedback | Was the suspected condition confirmed, and did the alert lead to useful work? |
| PUE, WUE, WUI, CUE, DCRE, server utilization, and IT Work Capacity | ASHRAE lists these as broader facility metrics. They describe different dimensions and should not be treated as interchangeable measures of maintenance effectiveness. |
ASHRAE’s 2026 AI Data Center Energy Performance Framework reports that U.S. data-center electricity consumption tripled from 2014 to 2023, reaching about 4.4% of national consumption in 2023. It also reports that U.S. data-center annual contribution to GDP nearly doubled from $355 billion in 2017 to $727 billion in 2023, and that new data centers in the ten U.S. states with the highest demand growth were associated with 10% electricity-demand growth from 2019 to 2023. These figures describe infrastructure and energy context; they do not demonstrate that AI maintenance delivers a particular energy saving or reliability improvement.
How to assess a monitoring or maintenance approach
When comparing systems or approaches, assess the operational fit rather than relying on an AI label. Compare:
- Asset coverage and supported equipment.
- Integration with existing data, controls, and maintenance records.
- How interpretable alerts are and how false alarms and missed detections are validated.
- Whether alerts can enter the CMMS or other documented work-order workflow.
- Cybersecurity, access controls, commissioning, and change-management support.
- Staff workload, training needs, and the ability to operate within facility procedures and applicable standards.
This is a practical comparison framework, not a published universal scoring standard. The facility should confirm that each proposed approach can be operated and maintained with its own staffing, procedures, systems, and risk controls.
Standards and scope
ASHRAE’s framework points readers to TC 9.9 thermal guidance, applicable codes and standards, formal operating procedures, commissioning guidance, Uptime Institute operations guidance, ANSI/BICSI 009-2024, and IFMA. Confirm current editions and local applicability before treating any referenced standard as binding. ASHRAE states that its framework is guidance; it does not establish mandatory requirements or supersede applicable codes and standards.
A facility-specific engineering and data-quality assessment is necessary to determine sensor needs, indicator limits, model design, and business case. The official sources cited here support implementation principles and examples, but do not establish that AI-driven maintenance outperforms every well-run condition-monitoring program.
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