How is it that the highest performing manufacturing plants stay ahead?
The answer lies in the strategic combination of maintenance and reliability activities that they carry out as opposed to a sweeping maintenance strategy across an entire organization. This targeted approach reduces downtime, boosts productivity, and saves money – but how is this balance achieved?
Knowing how to calculate reliability.
What do these metrics have in common? They rely on large-scale data collection. While collecting this volume of data was once manual and time-consuming, it can now be done rapidly at scale using affordable Industrial Internet of Things, or IIoT, devices that constantly measure your machine’s health and performance metrics. Modern, cloud-based computerized maintenance management system (CMMS) software can help manage your organization’s data with dashboards and easy-to-use reports.
The Impact of Reliability Calculations on Maintenance Strategy
To make their reliability and maintenance strategy more targeted and impactful, modern plants use a number of different key metrics to gauge their assets’ reliability and availability.
Two of the most commonly used metrics are Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR). Other metrics, like the system reliability formula, are also useful maintenance KPIs with insights into asset performance.
Calculating Key Metrics for Reliability and Maintenance Teams
- How do you calculate Mean Time Between Failure (MTBF)?
Mean Time Between Failure (MTBF) is a measure of the average length of time between asset breakdowns. It is calculated by dividing an asset’s uptime by the number of failures experienced in that period of time.If your MTBF is low, it means that your asset is breaking down often and needs frequent maintenance.If your MTBF is high, it means that your equipment can stay up and running for long periods before failing. MTBF is normally expressed in hours and tells you, on average, how long the machine will be able to operate before it’s expected to break down again.
Calculating MTBF can help determine whether to repair or replace a particular asset. If a piece of equipment has a very low MTBF, it may be worth investing some time and effort to try and improve this metric. If you consistently fail to improve the MTBF and the machine keeps breaking down shortly after maintenance, then it may be time to replace it. Knowing this frees up your maintenance team to put their time into an asset that responds better to repairs.MTBF can also help drive inventory decisions so that you always have spare parts on hand. If you know roughly when a component part is likely to break down, then you’ll know when to stock spare parts.
- How do you calculate Mean Time To Repair (MTTR)?
MTTR measures the average time required to repair an asset and return it to operation. It is calculated by dividing the total downtime by the number of repairs. A lower MTTR indicates faster repair times, which contributes to increased asset availability.
- How do you calculate Failure Rates?
An asset’s failure rate changes over the course of its lifecycle. When the asset is new, its failure rate is at its lowest point. But as the asset reaches the end of its useful life, its failure rate increases dramatically. You can calculate an asset’s failure rate using the same data as you’d use to calculate MTBF. Simply divide the number of failures by the number of operating hours.
- How do you calculate system reliability?
System reliability measures the dependability of a major asset, one which consists of a number of component parts. System reliability refers to the percentage of time that the major asset is available, without any failure or breakdown. The system reliability formula builds on the failure rate metric. Once you know the failure rate of each component part of an asset, you can use that to calculate the overall reliability of the entire system.
The formula looks like this:
R=(1−F1) ∗(1−F2) ∗(1−F3) ∗(1−F4) …
R refers to the overall reliability of the system, or asset. F1 refers to the failure rate of the first component part; F2 is the failure rate of the second component part, and so on. System reliability is a useful metric for anyone who wants to get an overall view of their system performance.With these metrics in hand, you and your team will have a more informed understanding of which machines are bad actors, those that are critical assets in disguise, and a better understanding of prioritization.
Data Collection for Reliability and Maintenance
Keep in mind that reliability calculation relies on effective large-scale data collection. Your metrics are only as good as the data that they are based on, after all.
While collecting a large volume of data was once manual and time-consuming, it can now be done rapidly at scale using affordable Industrial Internet of Things, or IIoT, devices that constantly measure your machine’s health and performance metrics. Then, modern, cloud-based computerized maintenance management system (CMMS) software can help manage this data with dashboards and easy-to-use reports.
With these tools, you will be able to track how often your assets break down, how often they require maintenance, and how many hours they are in operation. It’s also a good idea to track condition monitoring data like vibration measurements, temperature, power quality, and oil analysis. The more detailed and granular you can get, especially for your most critical assets, the better the picture of each asset’s reliability you’ll get. We’ll dive into this a little further in this article.
Using Reliability Calculations for Better Maintenance
But first, let us reassure you that the result of collecting data and calculating reliability is well worth the effort.
It bears repeating that the insights you will derive using reliability calculations will finesse your reliability and maintenance strategies because not only does it improve your understanding of equipment performance but also empowers you to implement advanced maintenance practices that can then significantly improve operational efficiency. Here’s how:
- Condition Monitoring with IIoT Technology
As mentioned earlier, IIoT technology has revolutionized condition monitoring, making it more accessible and effective than ever before with smart sensors installed on critical equipment continuously tracking parameters such as temperature, vibration, and power quality. These sensors provide real-time data that can be analyzed to detect deviations from normal operating conditions.By establishing a baseline of system performance, condition monitoring allows reliability and maintenance teams to identify potential issues early.For instance, an increase in vibration levels might indicate a developing bearing issue. Addressing such minor problems before they escalate prevents unplanned downtime and reduces Mean Time To Repair (MTTR). This proactive approach ensures that maintenance activities are timely and targeted, enhancing overall equipment reliability.
- Leveraging Predictive Maintenance
Condition monitoring data is the foundation of predictive maintenance, which moves beyond traditional scheduled maintenance to a more dynamic and responsive model. Predictive maintenance, as we mentioned earlier in this piece, uses a continuous stream of real-time data (from condition monitoring) to forecast potential failures before they occur. By analyzing patterns and trends, reliability and maintenance teams can predict when a machine component is likely to fail and schedule maintenance activities accordingly.
This data-driven approach to maintenance optimizes asset management by preventing unexpected breakdowns and extending the life of equipment. Predictive maintenance not only helps in maintaining high MTTR scores but also minimizes maintenance costs by avoiding unnecessary routine checks and focusing resources on actual needs. The result is a significant boost in productivity and efficiency, as machines operate with fewer interruptions and maintenance teams can plan their activities more effectively.
- Enhancing Maintenance Decisions with CMMS
As mentioned earlier, CMMS software can help in organizing, analyzing, and reporting data, making it easier for reliability and maintenance teams to make informed decisions.CMMS dashboards provide a visual representation of key performance indicators (KPIs) such as MTBF and MTTR, along with insights into the overall health of the equipment. These tools enable maintenance managers to prioritize tasks, schedule maintenance activities, and allocate resources efficiently. By integrating CMMS with IIoT devices, organizations can achieve a seamless flow of information that enhances maintenance planning and execution.For example, when Next Wave Energy Partners turned to eMaint CMMS, they found that its customizable solutions and flexible system architecture let them collect and analyze crucial data that their maintenance teams and decision-makers needed in order to manage 18,500 assets across a billion-dollar chemical plant. “We’re building all these metrics off the assets, such as Mean Time Between Failures (MTBF) and Mean Time To Repair (MTTR),” said Marcus Taylor, Maintenance Manager at Next Wave. “eMaint has been instrumental in helping us track our assets and get the data we need.”The New England Fertilizer Company (NEFCO) also uses eMaint CMMS to track a variety of key business metrics, including work order types, Mean Time Between Failures (MTBF), Mean Time Between Repairs (MTBR), and open work orders monthly. This has helped the organization ensure standardization, stability and consistency throughout all NEFCO plants.
With such precision tools at your command, turning data into decisive maintenance action will never be clearer.
Benefits of Using Reliability Calculations for Maintenance
A comprehensive understanding of asset health at your facility coupled with tools like a CMMS that helps your team act more decisively can lead to outcomes that are both immediate and far-reaching:
Improved Asset Performance
Regular monitoring and timely maintenance keep equipment operating at optimal levels, reducing wear and tear and extending its lifespan.
Enhanced Safety
Identifying and addressing potential issues early prevents accidents and supports a safer working environment for maintenance personnel.
Cost Savings
By focusing on actual maintenance needs rather than routine checks, predictive maintenance reduces unnecessary expenses and allocates resources more effectively.
Increased Uptime
Minimizing unplanned downtime through early issue detection and targeted maintenance activities keeps production lines running smoothly.
Data-Driven Decisions
Access to real-time data and analytics allows reliability and maintenance teams to make informed decisions, improving overall maintenance efficiency and effectiveness.
Enhancing Maintenance with Reliability Calculations
Reliability and maintenance are vital for efficient industrial operations. By leveraging reliability calculations and modern maintenance strategies, plants can achieve greater productivity and lower maintenance costs. Implementing advanced tools like IIoT devices and CMMS software helps in gathering and analyzing data, leading to better maintenance decisions and improved asset reliability.