Lifecycle Efficiency Online (LEO) is a system that collects data from a machine’s or equipment’s hydraulic cylinders. This data provides insights into both the operation and condition of the cylinder.
You can read more about LEO and how it works in our blog posts: “Predictive condition monitoring of hydraulic cylinders creates savings and efficiency in operations” and “How the implementation and installation process of predictive condition monitoring of hydraulic cylinders, or LEO, takes place”.
In this blog post, we’ll explain how LEO’s data can be used in different scenarios—and how it can support smarter decision-making.
1. For factory maintenance processes and their development
Factory maintenance typically aims to make machine servicing well-planned and scheduled in advance to minimize the impact of downtime on productivity. Data collected through LEO can support this goal by helping to plan maintenance and optimize maintenance processes. Since LEO collects data while machines are operating, it enables the detection of changes or anomalies in real time.
For example, LEO can be installed on a specific machine or production line to monitor hydraulic cylinder loads. In a factory setting where a machine performs consistent tasks, its cylinder usually operates under stable conditions. If LEO detects deviations in the cylinder’s load patterns, that cylinder can be flagged for closer observation. This makes it possible to schedule targeted maintenance and carry out any necessary repairs before failures occur—helping to prevent unexpected (and often costly) downtime and further damage to the cylinder.
2. For fault diagnostics over a specific time period
LEO can also be used to more precisely identify and diagnose faults in hydraulic cylinders. For instance, if a cylinder fails in a machine and the root cause of the failure is unclear, data from LEO can be used to investigate the issue. The data provides insight into how the cylinder was functioning during operation and whether it was subjected to pressure spikes or other stressors that could have contributed to the failure. In short, LEO’s data helps pinpoint the source of the problem and supports accurate fault diagnosis
3. As a digital learning tool
LEO can also serve as a valuable asset in training programs—both educational institutions and companies can use its data to support driver or operator training. Traditionally, training relies on experience-based advice about how to operate a particular machine or piece of equipment. With LEO, training can be backed by real operational data, showing how different driving styles impact machine performance. The data can highlight which practices are most efficient and extend the machine’s lifespan. By integrating LEO into training, it becomes possible to teach driving habits that reduce wear and tear, ultimately helping machines have the longest possible lifespan.
4. For cost comparison and calculation related to equipment
LEO can also be used as a tool for internal company calculations. The data it collects provides insights into how a specific machine is used and how much it’s consumed over a selected time period. This makes it easier to calculate the maintenance and upkeep costs of different machines.
For example, imagine one machine is used to break rock and another to dig sand over the same period. LEO can reveal how the machines were used differently and how each type of work affects wear and tear, as well as the associated maintenance costs. With this information, it’s possible to calculate the maintenance expenses tied to specific tasks. LEO’s data also allows for more accurate cost and margin calculations when planning and pricing projects.
