How to Implement Reliability-Centered Maintenance | Augury

Every modern reliability leader knows this challenge: teams are shrinking, but the cost of unplanned downtime is rising. With the responsibility of keeping your production lines running smoothly, there’s little room for error.

Right now, you’re probably managing that risk with a mix of strategies. Maybe it’s a calendar full of preventive tasks or spot monitoring. But these approaches leave gaps you can’t always see, and by the time a problem is loud enough for a technician to notice during a walkthrough, the damage is usually done.

This is where reliability-centered maintenance (RCM) comes in. This framework provides a clear logic for prioritizing your most critical assets and defining maintenance strategies based on the actual impact of failure and the specific functions of your equipment.

In this guide, we explain what RCM maintenance is, its key principles and benefits, plus a six-step roadmap for implementation.

Key highlights:

Reliability-centered maintenance (RCM) is a strategic framework designed to ensure that industrial equipment continues to fulfill its intended functions by focusing on asset-specific failure risks.
You can achieve significant operational gains through RCM strategies, including increased production uptime and reduced maintenance costs.
RCM implementation follows a six-step roadmap that starts with prioritizing high-impact assets and finishes with a continuous review cycle based on real-time performance data.
Industrial leaders who leverage continuous Machine Health monitoring can protect their most critical production lines and ensure maintenance reliability by targeting repairs only when data confirms a genuine risk to performance.

What is reliability-centered maintenance?

Reliability-centered maintenance is an approach that industrial leaders use to identify the most effective intervention for each asset based on its specific failure modes and operational impact.

Think about two identical pumps in your facility. One provides coolant to your most critical production line, while the other handles a secondary task such as general washdown. An RCM analysis recognizes that while the machines are the same, the consequences of their failure are not. You might choose to prioritize 24/7 asset condition monitoring for pumps in your most critical units, while opting for a simple run-to-failure strategy for the other equipment.

How do RCM strategies compare to other maintenance approaches?

RCM strategies serve as the foundation for defining the maintenance techniques you use for each asset in your facility, so you can have a clear business case for every hour your team spends on repairs.

While many plants still rely on fixed maintenance calendars, the industry is moving toward more dynamic models. According to the Machine Health Is Business Health report, the use of proactive maintenance approaches (including condition-based, predictive, and prescriptive) has increased to 82% as leaders seek more certain ways to reduce downtime.

Risk-based maintenance vs. reliability-centered maintenance

Risk-based maintenance (RBM) tells you which assets to prioritize, while RCM uses each piece of equipment’s specific failure modes to help you define the maintenance strategy required to keep it reliable.

RBM takes a top-down view of your facility, using matrices to rank assets by how likely they are to fail and the impact if they do. RBM outputs should help reliability leaders secure the budget for maintenance in high-risk equipment.
RCM works from the ground up, defining exactly what each asset needs to do and what could cause it to fail. For example, you might look at a pump’s required flow rate and pinpoint the failure modes that could disrupt it. Then, you can use RCM analysis to set up targeted actions to keep those functions running.

You can use an RBM-style criticality matrix as an initial step within the reliability-centered maintenance process to focus efforts on high-priority assets.

Reliability-centered maintenance vs. preventive maintenance

Preventive maintenance (PM) is a strategy in which teams perform regular maintenance checks at fixed intervals. RCM is the framework you use to audit and optimize those PM schedules. By leveraging RCM strategies, you can decide which assets need preventive inspections and which equipment to prioritize for continuous machine diagnostics.

See how to create your preventive maintenance checklist .

Predictive maintenance vs. reliability-centered maintenance

Predictive maintenance (PdM) uses real-time data from IoT sensors and AI-driven analysis to forecast when an asset is likely to fail. RCM provides the decision logic to help you understand which assets require a predictive strategy in the first place.

For example, an RCM analysis might show that an industrial compressor powering one of your production lines needs predictive monitoring to prevent a widespread shutdown. Meanwhile, a utility fan in a secondary area might only require a simple visual inspection. RCM sets the plan; PdM provides the data to execute it with certainty.

Let’s review how these maintenance approaches compare:


Maintenance approach	Core focus
Reliability-centered maintenance	Preserving system functions by identifying specific equipment failure modes and selecting the most effective intervention for each asset
Risk-based maintenance	Prioritizing facility-wide resources by using a risk matrix that weighs the probability of failure against its total impact on safety and production
Preventive maintenance	Performing routine service tasks and part replacements at fixed time or usage intervals, regardless of the machine’s actual condition
Predictive maintenance	Using real-time sensor data and AI-driven insights to forecast when a failure is likely to happen, so you can plan repairs in advance

Reliability-centered maintenance benefits

Implementing a reliability-centered maintenance program directly impacts your facility’s performance. Research from Deloitte shows that poor maintenance strategies can reduce an asset’s overall productive capacity by 5% to 20%. Aligning maintenance efforts with the actual needs of your equipment helps you maximize the value of every asset.

Key reliability-centered maintenance benefits include:

Increased production uptime: Identifying critical failure modes allows your team to prioritize the assets most likely to disrupt operations, so you catch any issues before they cause unplanned downtime.
Reduced maintenance costs: Minimizing redundant or unnecessary preventive tasks ensures you use budget and parts inventory only when the machine’s actual condition requires them.
Extended equipment life: Keeping assets within their performance standards prevents excessive wear and tear that occurs when machines operate outside their ideal parameters.
Better team efficiency and morale: Shifting from a reactive, firefighting culture to a data-driven strategy gives technicians a clear, manageable plan that reduces high-stress emergency work.

The 6 reliability-centered maintenance principles

In a high-stakes production environment, simply knowing a machine is “running” isn’t enough to guarantee reliability. Establishing functional outputs (e.g., ensuring a motor maintains a specific RPM) helps you protect the specific performance your business depends on.

These six reliability-centered maintenance principles lay the foundation for effective maintenance planning and operational excellence.


Reliability-centered maintenance principles	What this RCM principle means for your operation
1. Preserve system functions over basic operability	Define success by output, such as a pump maintaining a specific flow rate, rather than just checking if the motor is spinning
2. Focus on system-level performance, not just individual parts	Evaluate the entire production line to understand how the health of a single asset affects the overall efficiency of your process
3. Accept that failures happen, and most are not age-related	Monitor assets for early signs of failure to catch the defects that scheduled overhauls may miss, such as poor installation or operational stress
4. Prioritize failure modes by risk (chance x impact), targeting frequent or high-impact ones	Allocate resources based on criticality, focusing your efforts on failures that can cause the biggest safety or production consequences
5. Perform only necessary maintenance where benefits outweigh costs	Reduce “just-in-case” tasks by intervening only when the data demonstrates that maintenance will provide a measurable gain in reliability
6. Select best tasks (e.g., condition-based, runtime, or redesign) using decision logic	Apply the right tool to the job, such as using Machine Health for continuous monitoring on critical assets

What are the steps for RCM implementation?

RCM implementation depends on a logical progression: starting with a high-level view of your facility and narrowing down to the specific needs of each machine. By involving cross-functional teams from operations and process engineering, you build a strategy that is both technically sound and supported by the people who know your equipment best.

Here’s how to start a reliability-centered maintenance program:

1. Prioritize critical machines for analysis

Identify the assets that have the biggest impact on your facility’s safety, compliance, and production. Use a criticality matrix to zero in on the equipment that has caused the most downtime or driven up repair costs. By focusing your best people and resources on these high-impact machines, you can show quick wins and prove the value of reliability-centered maintenance.

Learn why and how to perform a criticality assessment .

2. Define asset functions and performance standards

Establish exactly what the machine must do to be considered “successful” in its specific operating context. For an industrial motor, this might mean maintaining a precise RPM while under a variable load to keep a production line perfectly synchronized.

Defining these functions is the first step toward meeting the SAE JA1011 standard, which ensures your RCM process is technically sound. Having clear definitions gives you the necessary guardrails for all future maintenance activities and helps your team spot a functional failure when a machine falls short of its required output.

3. Identify the equipment’s failure modes

Examine every way an asset might fall short of the performance standards you set. For example, if a conveyor belt runs but cannot reach the speed your production requires, that is a functional failure you need to document.

Failure Mode and Effects Analysis (FMEA) is your main tool for this step. This approach provides a structured way to list every credible cause of failure. After uncovering these root causes, your team can take the specific actions needed to prevent failure before it interrupts your schedule or compromises safety.

4. Assess the consequences of an asset failure

Look at how each failure mode could affect safety, the environment, and your business performance. Focus on both how likely the failure is and how severe the impact could be. This step helps you understand the consequences of each machine breakdown, such as hazardous spills or downtime costs, and ensure your resources are always directed toward the highest-priority risks.

5. Determine the right maintenance strategies

Select an effective maintenance strategy to manage each failure mode. You must match the intervention to the specific risk, choosing between asset condition monitoring, scheduled checks, or even a deliberate run-to-failure approach for non-critical parts.

See the difference between preventive, predictive, and prescriptive maintenance .

6. Implement the RCM program and review

By collecting data from the plant floor, you can measure how well your RCM strategies are working. This insight lets you review maintenance tasks frequency or methods to match the actual condition of your equipment.

Reliability-centered maintenance: Examples from the production floor

Reliability-centered maintenance examples from real facilities show how RCM turns data into decisions, not just for individual machines, but across entire operations.

Consider a multi-site manufacturer running six or seven plants with hundreds of rotating assets. When their reliability team reviewed machine health data across facilities, three failure modes kept surfacing across every plant: misalignment, lubrication issues, and bearing wear. Rather than treating each instance as an isolated repair, business unit leaders used this insight as an RCM trigger, building a targeted, system-wide training response starting with alignment best practices.

The training drew directly from the plants’ own fault card data, showing technicians exactly how misalignment was affecting asset reliability and power consumption. The results were immediate. Sites that had been running machines in alarm status were able to restore them to healthy status using alignment tools they already owned. The team has since moved on to lubrication training across the business unit, working systematically through the failure modes their data identified.

These failure modes are not unique to one industry. Misalignment, improper lubrication, and bearing wear appear across manufacturing sectors and can cost facilities considerably in unplanned downtime and premature asset replacement.

That progression, from data to failure mode identification to structured training and remediation, is RCM in action. The maintenance strategy follows the risk, not the calendar.

Strengthen your RCM strategies with Augury’s Machine Health

Augury supports your RCM strategies by providing continuous, AI-driven condition monitoring for all your equipment. Our Machine Health Solutions give reliability leaders visibility into the specific faults, root causes, and historical machine data needed for high-level decision-making.

A Forrester Total Economic Impact™ study found that when organizations use Augury’s prescriptive solutions to continuously monitor assets, they optimize their resource allocation and reduce “unnecessary interventions”. For a composite organization managing 2,500 assets, this approach resulted in a 15% decrease in per-asset maintenance costs, totaling $1.5 million in savings over three years.

Frequently asked questions

What is the history of reliability-centered maintenance?

The history of reliability-centered maintenance traces back to a critical shift in how we perceive the relationship between asset age and failure.

Before World War II, most facilities simply fixed machines when they broke. Wartime production and post-war growth changed that, pushing the industry to adopt scheduled, time-based maintenance. This shift laid the groundwork for maintenance and reliability as specialized fields, but it still rested on the idea that parts fail simply because they age.

The real breakthrough happened in the 1960s and 1970s within the aviation industry, where researchers discovered that increasing the frequency of machine overhauls often caused more failures rather than preventing them. By analyzing years of flight data, they’ve found that most pieces of equipment don’t follow a predictable, age-related wear-out pattern; instead, they fail due to other causes, such as operational stress, poor installation, or power surges.

These findings culminated in the 1978 landmark report by Stan Nowlan and Howard Heap, which coined the term “reliability-centered maintenance” and provided the structured framework used today to prioritize system functions over individual component age.

What is the overall goal of reliability-centered maintenance?

The overall goal of reliability-centered maintenance is to ensure that industrial assets continue to perform their intended functions in their specific operating context.

Unlike approaches that focus on keeping individual components in “like-new” condition, RCM prioritizes the reliability of the entire production system by identifying failure modes that most affect safety and output, ensuring that maintenance efforts are directed toward protecting the most critical parts of the industrial process.

What is RCM analysis?

An RCM analysis is the technical process that determines the actions required to keep a component performing as intended within its operating context. This evaluation focuses on the unique ways a piece of equipment can fail and the actual consequences of those failures.

A complete RCM analysis answers these questions for each asset:

What are the functions and standards of performance?
How does functional failure occur (failure modes)?
What causes each failure mode?
What happens when failure occurs (effects/consequences)?
How severe are the consequences?
What are the most applicable maintenance tasks?
What actions should the team take if no effective tasks exist?

By working through these questions, reliability leaders ensure that every maintenance task is technically justified and directly linked to preserving a critical system function.

What are the technology requirements for implementing a reliability-centered maintenance process?

While technology isn’t a strict requirement for performing RCM analysis, it can help you scale the program into a living strategy. Using an RCM tech stack enables you to manage a massive volume of failure data, automate maintenance triggers, and predict equipment failures in advance.

Technology that supports your reliability-centered maintenance process includes:

Computerized maintenance management system (CMMS): Centralize asset histories, manage work orders, and track the specific maintenance tasks your RCM analysis identifies.
Condition monitoring sensors: Deploy IoT hardware to capture real-time machine data on vibration, temperature, and magnetic flux, allowing you to execute the condition-based tasks RCM prioritizes.
Predictive analytics and AI: Apply machine learning to process sensor data and identify the failure modes that traditional, calendar-based schedules can miss.
Data integration layer: Connect your shop-floor sensors to your CMMS via APIs to ensure that an alert automatically triggers the correct, RCM-justified work order.

Augury provides the real-time data that backs up your RCM strategies and protects your asset fleet. Our expert-backed AI detects specific failure modes from your machines, giving you clear instructions to fix them before they cause downtime.