In a recent plant reliability survey, 60% to 70% of industrial facilities consider oil analysis an important part of their reliability programs. Oil analysis gives a snapshot of machinery health, preventing unnecessary oil changes and assisting in predicting equipment failures. This article will take a detailed look into using data to decrease maintenance costs and increase the bottom line. Being able to extend oil drains or even shorten them to eliminate failures can be an easy way to reduce maintenance costs, but data must be available that allows for making those decisions. This article will address the role of key performance indicators (KPIs) in predictive maintenance, how to gather useful data that aligns with KPIs and review a few case studies where onsite labs were able to use data to take advantage of warranty periods, justify keeping assets after warranty and extend the interval between oil drains to reduce oil consumption.
Oil Analysis Introduction
Lubricant analysis is much like a blood test for humans. By trending the correct parameters like blood pressure and cholesterol, the patient gains an understanding of overall health. Deviations in those trends over time indicate that action needs to be taken. The same concept can be applied to machinery health when looking at the three key areas of oil analysis: wear, contamination and chemistry. Using data in these three areas can lower overall maintenance costs, reduce unplanned downtime and increase asset life. Within a plant setting, oil analysis is often paired with several other technologies that encompass the Condition-based Maintenance (CBM) Program. The most common technology seen is vibration analysis. Typically, vibration analysis picks up on faults just a little bit later in the failure progression process than oil analysis, which is why they are typically paired together. Infrared thermography and motor circuit analysis are also used from time to time. Pairing technologies together gives confidence in the results and helps the engineer make critical decisions (if needed) to take a machine offline or remove it from service.
Figure 1 shows a typical machine failure curve. The diagram illustrates that oil analysis tests like viscosity, elemental count and particle count are useful parameters to trend even when equipment condition is considered satisfactory. Any issues detected would still be early enough in the failure process to perform the necessary maintenance far in advance of an actual failure. This keeps the cost of repairs relatively low. As the machine failure progresses, abnormal wear can be detected and still addressed early in the process to keep costs under control. Typically, the cost of repair goes up, and production time is lost the later in the process that failure occurs.
Establishing Oil Analysis KPIs
Strong reliability programs typically have Key Performance Indicators (KPIs) tied to company financial goals. KPIs are measurable values that demonstrate how effectively a company is achieving key business objectives. Understanding the company goals is typically going to come from the top down and likely will strictly be expressed in numbers. A manager may say maintenance costs are cut by 25% and end there. If cutting costs by 25% is the main goal of the year, it must be broken down into tangible goals that the maintenance department can achieve. Some tangible examples include increasing the production of machines by 25% and/or reducing oil consumption by 25%. However, these are still broad KPIs that need to be broken down even further to achieve the goal. The question then becomes, how do we reduce oil consumption by 25%? Extend oil changes? Sweeten oil when necessary instead of draining the entire reservoir?” Data helps drive these decisions.
Data is powerful, but it must be the correct data that aligns with the KPIs to achieve company goals. If trying to increase production on a particular machine by 25%, it is necessary to reduce the chances of unexpected downtime. Unexpected downtime can be related to several issues, some of which relate to either improper equipment installation or lubricant contamination issues. A great place to start is lubricant contamination issues and getting control of moisture and particle contamination. Typically, about 80% of machine failures can be traced back to particle contamination. By starting with filtration, the chances of increasing uptime are strong. Increasing bearing life can also be related to lubricant cleanliness. Reducing oil consumption can be related to contamination and preservation of the oil and additive chemistry. Extending oil changes will be related to preserving and maintaining the integrity of the oil chemistry and keeping the oil clean. For additional assistance in developing test slates and choosing the correct parameters, ASTM Standards and ICML guidelines are available; ASTM D6224, ASTM D4378 and ICML 55.1 Section 7 can be used.
Onsite Oil Analysis Solutions Support Reliability
Advances in software that integrate the expert knowledge of the onsite equipment specialist and lubricant analyst are available to help maintenance professionals justify oil analysis programs within their facilities and maximize equipment life. Understanding how to quickly and effectively implement Industry 4.0 techniques becomes increasingly important in an era when many maintenance programs are being downsized or absorbed by other areas of the company.
Effective oil analysis techniques that provide value to the facility require the incorporation of two main concepts: knowledge of the component behavior and an understanding of the lubricant data generated by the component. By pairing these two concepts, proper diagnostics and recommendations can be made that are practical and easily implemented by the maintenance staff.
Connecting Globally
Enterprise thinking is the practice of considering the entire organization in the decision-making process, not just an individual department or group. Enterprise thinking can make the organization leaner and more agile.
Enterprise solutions typically develop at the corporate reliability level with the intention of promoting efficiency, consistency and a more system-wide approach to the complex process of asset management. Networking of knowledge is an efficient way to share information over a certain platform at varying locations. This mentality works best when an organization already has a well-established site and can easily share that information with other locations or parts of the organization. Organizations that have standard equipment across multiple sites, have similar KPIs or use the same software are good candidates to deploy enterprise solutions.
Figure 1: Machine Failure Curve showing the typical
progression of problem to failure of the component.
Onsite Analysis Case Study: Food and Beverage Industry
In the Food and Beverage Industry, there are two processes where oil analysis can play an important role: the washdown process and the drying process. In food processing applications, proper cleaning and sanitation are key to producing a safe product. Cleaning and sanitation involve a washdown process. They usually have 16-18 hour shifts of running equipment, then shut down for 4-8 hours for the complete washdown and cleanout of the facility. During this process, water contamination is of primary concern. For the dry processes, like breakfast cereal, fine dust particles in the air can enter the oil. In this case, oil cleanliness is of primary concern.
In the Food and Beverage Industry, the real interests lie in increasing availability and avoiding shutdowns and unnecessary spending. Here is an example:
- A plant has 1000 assets.
- 10% are considered critical equipment (100 of them are critical).
- Overall equipment availability is 93%.
- Total downtime is 58,800 hours.
- Downtime Cost = $200/hour.
- Equates to about 11.8M annual downtime cost.
- Assuming implementing an onsite oil analysis program improves the availability by 0.1% (from 93% to 93.1%).
- Drive down the downtime by 840 hours (from 58,800hrs to 57,960hrs).
- Equals to $168,000 in annual downtime cost savings.
Conclusion
Maintenance cost savings and an increase in productivity continue to be driving forces for implementing condition monitoring programs. Lubricant analysis plays a critical role in a condition monitoring program and pairs well with other technologies such as infrared, ultrasound and motor circuit analysis. Being able to quickly and effectively implement the data is now a reality with advancements in onsite techniques. When adding onsite oil analysis to a facility, the management and proper distribution of the data to equipment owners becomes pivotal in justifying program costs and sustaining a reliability program. Software that captures expert knowledge of the machine behavior and lubricant are both critical in creating analysis reports that are helpful and easily implemented at the plant level. Once those initial rules, limits and observations are captured by the expert, enterprise tools and other features can be deployed company-wide to synchronize reliability programs within an organization.
