In any industrial setting, ensuring that assets are functioning correctly is essential. Failure Modes, Effects and Criticality analysis (FMECA) is a widely used method to evaluate the reliability and safety of equipment. However, FMECA has some drawbacks that make it less efficient and scalable than alternative methods like asset modelling and asset analytics.
FMECA is a structured approach to identifying potential failures, their effects, and their consequences on equipment or systems. It involves analyzing the system or equipment to identify all possible failure modes, their effects, and the likelihood of each failure occurring. The severity of each failure mode is also evaluated to determine its criticality. This analysis helps to identify potential improvements in the design or maintenance of equipment, making it safer and more reliable, however for analytical purposes, using a FMECA approach is sometimes a constraint.
FMEA was developed by the United States military in the 1940s as a risk identification tool. From FMEA, the Failure Modes, Effects and Criticality (FMECA) analysis was born. The new methodology incorporated a criticality analysis to rank failure modes in their order of importance.
A FMECA captures the links between the way an asset fails and the impact of its failure on the business. Historically, FMEA was used during the design phase of a project, to identify and rectify potential failures before going into production. However, understanding the relationship between failure modes and their effects is a key component to making decisions relating to an asset’s operation and maintenance. Nonetheless, a FMEA or FMECA is only part of the necessary information to make informed decisions and should not be considered in isolation.
Nowadays, FMEAs and to a greater degree, FMECAs are used to inform and improve maintenance efforts throughout the life of an asset. Failure mode and effects relationships typically form the basis of more advanced methodologies such as asset modelling. More progressive analyses (such as modla's platform) combine FMEA structures with statistical distributions to predict likelihoods of specific failure modes. This enables users to produce results for probabilistic scenarios with varying maintenance, operational and intervention strategies.
Asset modelling is a better approach than FMECA for evaluating asset reliability and safety. Asset modelling involves creating a digital twin of the asset, which captures all relevant information about the asset's design, components, and operational parameters. This model can be used to simulate the asset's behavior and predict potential failure modes.
Asset analytics involves using machine learning algorithms and other advanced analytics techniques to analyze large amounts of data generated by assets to identify patterns and predict potential failure modes. Asset analytics can also be used to optimize asset performance and reduce maintenance costs.
Generating FMECAs is a quick and easy three-step process:
Extracting a FMECA from our platform is this fast!
A failure mode describes a way in which a system or component may fail, meaning it can no longer perform its function.
Effects describe the impacts of a failure on the business, environment, stakeholders or otherwise. They answer the question: “If this asset fails in this way, what happens?”
The analysis puts forth a structured or stepwise method in which the FMEA/FMECA is carried out by identifying failure modes of a process or product, as well as their impacts.
Criticality (optional) identifies the asset's importance to the business. There are two main schools of thought:
The main components of performing a FMEA/FMECA are:
Modla's platform can produce FMECAs based on a probabilistic analyses that incorporates your specific environmental and operational nuances.
For some, criticality is simply the consequence of failure regardless of its associated likelihood. This approach was historically used to rank consequences such as safety incidents, which at the time, were largely qualitative.
In recent years, more and more businesses are turning to quantitative assessments of risk and monetising everything from environmental impacts, to the stakeholder and reputational loss. This can be achieved by using a corporate value framework.
To understand which approach to use, the question must be asked: “Why are we trying to rank our assets in the first place?”
If the reason is to understand potential outcomes and make sure that each has a mitigating task or control in place, then a consequence only approach is suitable since this is a binary decision i.e is the outcome acceptable or not?
The problem with this approach is that businesses usually have finite resources, and trade-offs must be made. Are you willing to bankrupt the business in order to prevent an environmental spill? Probably not. Are you willing to spend a few thousand dollars? Probably. The spectrum of cost-benefit is what needs to be understood to make better decisions than the black and white decisions of the past.
The more information we have, the better decisions we can make. The likelihoods of failure is thus a valuable addition to the ranking process.
Conversely, criticality can be defined as a measure of risk. Risk includes statistical probabilities and is defined as:
Risk = probability * consequence
By using the risk definition for criticality, better decisions can be made by understanding both the likelihood and consequence of an event. For example, high probability, low consequence events can still produce a large risk (think employees slipping and tripping). This school of thought is particularly applicable to industries with high volumes of assets, but with low probabilities of failure (e.g. electricity transmission, where there are millions of poles and conductors). Since risk is summative, the approach is scalable.
Modla moves away from the risk matrix classifications of probabilities of failure and consequences. This approach treats risk as a spectrum, and not discrete levels.
Following on from the formula, risk = probability * consequence, we can expand this to:
Risk due to a specific failure mode = probability of failure mode occurring * (probability of consequence 1 * consequence 1 + ... + probability of consequence n * consequence n)
The presence of a specific failure mode is dependent on the asset in question. Similarly, the probability of the consequence is dependent on the context of the asset e.g. where is it located and are there redundancies in place. Both the probability and consequence calculation may take into account:
There are several benefits to FMEA/FMECAs, and its structure is a key building block our Reliability Centered Maintenance + Decisions (RCMD) methodology.
Apart from compliance with various safety and quality requirements e.g. ISO 9001, ISO/TS 16949, Six Sigma, QS 9000, FDA Good Manufacturing Practices (GMPs), FMECAs capture engineering knowledge which can inform troubleshooting efforts, identification of maintenance tasks and more.
To paint a complete picture, the FMECA must be combined with other information such as maintenance task costs and resource information in order to improve maintenance and operational strategy.
The FMECA structure, as well as other causality information, forms the basis of modla's knowledge base (see knowledge management). A criticality analysis should be performed on every asset. However, the main barrier to its wider adoption is the cost (monetary and resources). Modla's platform reduces the cost to perform a FMECA to the point where it is no longer cost-prohibitive to perform a FMECA on all assets.
FMEA/FMECAs are of fundamental importance to maintenance teams. It can be used to improve the reliability of assets during the design phase and improve performance long after by optimising strategies and capturing engineering knowledge. FMECAs allow businesses to review the criticality of competing failure modes and to prioritize the application of resources. However, the quality of this analysis is highly dependent on the quality of the associated data and knowledge used as inputs.
While FMECA has been a useful tool for evaluating the reliability and safety of equipment, its limitations in scalability, knowledge capture, and continuous improvement make it less efficient than asset modelling and asset analytics. By adopting a knowledge-first approach through asset modelling and analytics, organizations can reduce costs, optimize asset performance, and increase reliability and safety.
Therefore, organizations should consider investing in knowledge-first architectures to develop and implement effective asset modelling and analytics programs, which provide a more scalable, adaptable, and efficient approach to asset management.