How to evolve from preventive to predictive maintenance
Planned interventions
The maintenance of industrial machinery is directly linked to the efficiency of production processes and, consequently, to companies’ profitability. A machine’s working time is money, hence the importance of ensuring it performs at its best.
In recent decades, equipment care has been carried out through planned interventions based on the time factor: the device’s lifespan and hours of use. The goal of what is known as preventive maintenance is to avoid breakdowns and failures that cause unplanned downtime, through scheduled corrective actions. Under these parameters, operational schedules are designed based on generic manufacturer guidelines and estimates of use, wear, or deterioration made by operators… regardless of the machine’s actual operating condition.
Preventive maintenance, in short, seeks to anticipate problems that may arise from the standard operation of equipment, through inspections and scheduled review and replacement actions. Under this approach, parts that experience greater wear are periodically replaced based on estimated deterioration, which may or may not hold true depending on factors such as temperature increases or decreases, for example.
This system ensures continuous maintenance of industrial machinery, but it leads to many unnecessary actions, such as replacing parts that still have useful life left, an excess of scheduled downtime for inspections, or spending the maintenance team’s time on recurring tasks.
Predictive maintenance: schedules tailored to real needs
Like all business areas, maintenance has undergone a major evolution in recent years driven by the digital transformation of production environments. It has made a huge leap from prevention to prediction. And what does this mean? The answer is clear: countless benefits.
Predictive maintenance gives companies detailed analyses of machinery performance, enabling them to know the optimal time—looking ahead—to carry out the required maintenance, avoiding unnecessary downtime and the replacement of parts that still have useful life, while ensuring the detection of abnormal wear as soon as it occurs, for example.
Let’s look further. Predictive maintenance is based on the actual performance of equipment, rather than a fixed schedule of interventions unrelated to its real needs; in other words, it moves from fixed schedules to dynamic ones. To achieve this, it relies on harnessing the data that each machine generates second by second, or even in smaller fractions of time. Variables such as temperature, vibration, current, or pressure are monitored and, when processed through algorithms and Artificial Intelligence, provide a real-time picture of the equipment’s true condition.
To simplify, since many variables are monitored and interconnected, an increase in vibration in one area may indicate abnormal deterioration of a part that needs to be repaired immediately to prevent further damage and prolonged downtime—something that would not happen with preventive maintenance, since inspections are carried out on a scheduled basis that may not coincide with the unexpected and non-obvious wear of a particular part. Similarly, continuously measuring the correct parameters eliminates the need for scheduled downtime for reviews or maintenance, which will only be carried out when truly necessary, resulting in more production time and, therefore, greater profitability.
The foundation of predictive maintenance, and the major difference it offers compared to previous systems, is, as we can see, constant monitoring, which provides a complete, real-time view of equipment health, enabling actions to be scheduled under a data-driven approach. It therefore anticipates the equipment’s needs; gives maintenance teams the information they need to optimize line care processes, gaining production hours, and provides early alerts of anomalies that require intervention, preventing more serious breakdowns that are costly in both time and money. It also eliminates the dependency on having someone familiar with the equipment physically present at the machine.
As a result, companies move away from interventions triggered by unexpected failures (which disrupt production) and scheduled inspections and part replacements (which fix failures that haven’t occurred and may not occur in the short term) toward working based on what is actually happening with each machine, at every moment. Predictive maintenance therefore delivers significant cost savings, since, in addition to predicting failures that may occur based on monitored variables, it allows work to be scheduled in advance, providing full control over timing, staffing, and required supplies.