What Is Reliability Centered Maintenance?
Reliability Centered Maintenance (RCM) is a method for developing a comprehensive reliability-based maintenance and monitoring program. RCM involves analyzing the Failure Modes and Effects Analysis (FMEA) of each piece of equipment to determine criticality; then deciding the appropriate and effective proactive maintenance, operation, or engineering tasks to preserve system function. This approach is valid for and has been applied successfully in systems and units within most process industries, such as electric power generation (nuclear, fossil, hydro), transmission and distribution, petrochemical, refining, upstream, midstream, manufacturing, paper, pharmaceutical, and water and wastewater treatment. The overall purpose of performing an RCM analysis is to develop a cost-effective and applicable proactive maintenance program for the system or unit under study. The evaluation includes:
- Identifying the performance standards that set the criteria used in determining the effect and tolerability of equipment failure
- Evaluating each piece of equipment integral to the operation of the system or unit
- Identifying the most-likely modes of failure of each piece of equipment
- Stating the effects of these failure modes
- Selecting applicable and effective proactive tasks to address the identified failures, including the recommendation of no proactive maintenance for certain equipment
RCM has traditionally been implemented as a one-time study that follows a rigorous set of questions designed to identify the modes and effects of equipment failure and define a set of tasks to prevent or mitigate these failure modes. Equipment criticality is defined based on the consequence of the identified failure modes and tasks that were preferentially applied to the critical equipment over the non-critical equipment. These traditional RCM methodologies tend to rely on industry-accepted data and personnel experience to determine appropriate failure modes and mitigating tasks.
History of RCM
An enhancement to the traditional RCM process was the introduction of analyzing risk into the methodology. Risk is defined as the product of the probability of failure (PoF) and the consequence of failure (CoF). Most RCM analyses in the past 20 years have incorporated the probability of failure into the analysis to define equipment criticality by developing risk matrices for various criteria such as safety, environmental impact, production loss, financial impact, and reputation impact. An added benefit of using risk matrices is the ability to add a gradient to the level of criticality. The added levels of criticality on the risk matrices allow for an improved prioritization of the proactive tasks identified by the RCM analysis. For example, proactive tasks for high-critical equipment have a higher priority than proactive tasks for low-critical equipment.
How Can Data-Driven Reliability Improve RCM?
Data-Driven Reliability is the framework for reliability improvement that connects results from your RCM model to your business decisions. This framework leverages reliability intelligence, a unique combination of traditional models, data science, and subject matter expert knowledge to equip facilities with the information they need to drive sustainable operations. Whether you choose traditional RCM or QRCM, data-driven reliability recognizes that it is about the quality of the data you collect and make sure that it is the right data that fuels these models.
What is the Value of Reliability Centered Maintenance?
The value of an individual Reliability Centered Maintenance (RCM) study will vary for each analysis, based on current equipment reliability, amount of change from the current proactive maintenance program, and market conditions. Typically, the value of an RCM study can be calculated over time based on increased availability, increased throughput, and/or reduced maintenance expenditures.
Implementation of the recommended tasks from the RCM study will yield the following benefits:
- Increased Availability and Reliability of systems and equipment evaluated
- Optimized Preventive Maintenance Program
- Decreased lifecycle maintenance costs by:
- Increasing planned (proactive) work and reducing reactive work
- Emphasizing Condition Monitoring and Predictive Maintenance over time-based intrusive tasks
- Eliminating activities that do not add value
- Optimizing the interval at which activities are performed
- Instituting the basis to keep the maintenance program evergreen
- Documented basis for prioritizing turnaround or Shutdown Maintenance
- Documented basis for optimizing spare partsinventories
- Identified gaps where training or new procedures are needed
- Identified one-time opportunities for improvement or vulnerabilities that cannot be addressed by a traditional maintenance program
- Identified need for Root Cause Failure Analysis
- Documented FMEA with tasks for individual equipment that can be used as a training tool
Pinnacle recently worked with a wastewater treatment facility undergoing an estimated $1.7B expansion to increase treatment capabilities due to stricter regulations and increased supply demand. Learn how we supported the facility by introducing Reliability Centered Maintenance (RCM) principals during the design phase to create a cost-effective way to maintain critical assets.
Learn how a global petrochemical leader wanted to improve safety, compliance, and operating costs through a non-fixed asset program to effectively manage operating risks. Pinnacle supported the organization with a Reliability Centered Maintenance program that included a review and update of the equipment list in its CMMS, FMEA, spare parts optimization, identification of reliability opportunities and vulnerabilities, and PM optimization.
What is the Benefit of a Pinnacle-Facilitated Reliability Centered Maintenance Study?
Primarily, the Pinnacle RCM methodology is designed to minimize the impact on customer resources. Several companies have had a poor experience with RCM implementation due to excessive time requirements to complete the analysis of a few major pieces of equipment. Our technique allows for the analysis of an entire unit in a similar amount of time. All maintainable equipment is included in the scope of our studies. This includes fixed equipment, rotating equipment, electrical equipment, instrumentation, and actuated valves. Detailed inspection recommendations for fixed equipment and piping are deferred to a specialized Mechanical Integrity (MI) or Risk-Based Inspection (RBI) analysis. The analysis typically focuses on process equipment, but non-process equipment (HVAC, safety equipment, firefighting equipment, material handling equipment, material handling equipment, lighting) is often included in a comprehensive, proactive maintenance program.
The Pinnacle RCM process is a methodical, efficient, and common-sense approach to developing a reliability-based maintenance and monitoring program that conforms to accepted RCM standards. Each step in the process is foundational as each subsequent step presupposes completion of the steps preceding it. The basic steps in our RCM process are outlined below.
Step 1: Data Collection
- Equipment List
- Equipment details
- Piping and Instrument Diagrams (P&IDs)
- Single Line Diagrams (SLDs)
- Process description
- Related reliability and safety analyses
- Cause and Effect Diagrams
Step 2: Performance Objectives
- Production and Throughput Targets
- Product Quality Specifications
- Safety and Environmental Compliance Targets
- Availability Targets
- Tolerance for Downtime
- Factors that Limit Run Length
Step 3: Functions
Step 4: Process Interview
- What is the component function? (What it is and what it does)
- How does it fail?
- What is the effect of failure?
- Is the failure evident? (especially for standby equipment)
- What is the operator response?
- Is the equipment/component/system performing properly and reliably?
Step 5: Failure Modes and Effects Analysis (FMEA)/Criticality Evaluation
Step 6: Task Selection
Step 7: Implementation
What Does the Future of Reliability Look Like?
The next enhancement to the Reliability Centered Maintenance analysis will be quantifying much of the process and using data in conjunction with traditional methods and Subject Matter Expert (SME) expertise to define failure modes and their probability of failure. This data can originate from the computerized maintenance management system (CMMS), the inspection data management system (IDMS), and rounds and readings from operations. The use of this data can improve the quality of the RCM analysis and can be used to keep the analysis evergreen over time.
Over the years, Reliability Centered Maintenance (RCM) has been a valuable method that has helped complex asset-based systems effectively maintain asset reliability through cost-effective strategies. Now, decades later, advancements in data acquisition, warehousing, modeling, and analytics are creating opportunities to improve upon the RCM model. The next leap in reliability will further improve availability while continuing to reduce maintenance spend.
An RCM program is the first step after a reactive program in the evolution of maintenance program maturity. However, regarding further optimizing and improving reliability performance, Reliability Centered Maintenance can be limiting depending on the objective. Specific limitations include the following:
- RCM does not calculate absolute risk but rather relative risk, using that to drive maintenance priorities rather than an objective cost/benefit analysis.
- RCM models are typically very conservative in assigning the probability and consequence of failure because the likelihood that the event or failure mode would occur most often stems from SME knowledge.
- RCM analyses are static by nature and typically do not incorporate the condition data of the asset to update the risk calculations (especially the Probability of Failure calculations) to drive maintenance priorities accordingly.
- RCM calculations occur on an asset-by-asset basis and generally do not relate to the overall performance of the system, unit, or facility.
- Although RCM program architectures are generally similar (criticality analysis, FMEA, etc.), they are subject to interpretation regarding how the analysis is structured.
- RCM does not help quantify the value of data collection or help with sensitivity analysis of required data for calculations beyond the manual iteration of values from the user.
- RCM cannot be used to optimize an entire system, unit, or facility’s reliability strategy based on availability, cost, and resource constraints.
Whether you are just starting to implement an RCM program or are already using a mature program, you have the necessary tools to make the next leap in reliability. This leap is being made possible through Quantitative Reliability Optimization (QRO). QRO is a method that pushes RCM to the next level by unlocking its capabilities using dynamic data analysis. Using actual asset data, QRO provides detailed insight into understanding when an asset will fail, identifying the impact each asset has on the whole system, and knowing how and when to use resources will improve the results you care about.
QRO is an approach to reliability modeling which connects every relevant reliability data point to one integrated model that enables users to do the following:
- Optimize, in near real-time, all maintenance spend based on short/mid/long-term reliability targets.
- Understand the economic value of every maintenance activity based on dynamic condition models (such as Probability of Failure) that update in a near real-time manner as new data is introduced in order to quantify where an asset is on its P-F Curve to determine when tasks should be performed.
- Understand the quantifiable impact each asset has on availability
- Understand the economic value of every piece of data that is currently being gathered or possibly gathered in the future.
- Simulation analysis to evaluate the impacts of maintenance and operational activities, including the implications of moving a turnaround, feedstock pricing changes, or various capital projects.
- Drive effective economic decisions in the event of reliability based operating excursions.