Effective management of industrial equipment maintenance is crucial for ensuring productivity and minimizing downtime.
Many strategies are available to companies for their maintenance, but reactive maintenance (firefighting) is often favored.
The reactive approach is a good first practice, involving responding as soon as a problem occurs. Effective internal communication is essential to quickly mobilize the maintenance team to intervene immediately. To be optimal, the response time must be measured, which can be identified if an alert is triggered by the operator, for example.
In this article, we will explore two other maintenance approaches that can be just as beneficial for companies: preventive maintenance and predictive maintenance. We will explore these two methods, provide some examples to help you understand their operation, and determine which might be most suited to your needs!
Preventive maintenance is a traditional, "mechanical" strategy that involves regularly scheduled inspections and preventive repairs at fixed intervals. This method relies on predefined schedules and replacement thresholds based on past experience or the machine manufacturer's recommendations. It is generally based on generic information that is neither precise nor tailored to the specific machine, process, or environment, and it is also not available in real-time. The main goal is to prevent failures before they occur by replacing or repairing parts before they wear out or become faulty.
✅ Prevention of unexpected breakdowns.
✅ Advance planning of maintenance.
✅ Reduction of unexpected downtime.
⚠️ Can be costly in terms of labor and parts.
⚠️ Risk of replacing parts that are still in good condition.
⚠️ Requires precise planning and dedicated resources.
Predictive maintenance, on the other hand, relies on the collection and analysis of real-time data from equipment. This approach uses sensors, advanced monitoring systems, and data analytics to predict when a machine might fail or require maintenance. Artificial intelligence and machine learning are often used to interpret the collected data and anticipate potential problems.
The real-time and accurate view of machine conditions allows for maintenance only when necessary, avoiding systematic action simply because the manufacturer recommends it. For example, replacing a filter is done only when it is actually damaged. This approach optimizes maintenance based on the actual condition of the machine, avoiding unnecessary interventions and ensuring maximum equipment lifespan.
The Institute of Electrical and Electronics Engineers found that a power production company reduced its maintenance costs by 25% and improved operational efficiency by 30% by integrating AI-based predictive maintenance solutions.
✅ Reduction of maintenance costs through optimal use of resources.
✅ Avoids unnecessary replacement of parts.
✅ Minimization of downtime through targeted intervention.
✅ Increased equipment lifespan.
⚠️ Initial investment in sensors and monitoring systems.
⚠️ Requires expertise in data analysis and artificial intelligence.
⚠️ Accuracy depends on the quality of data and the prediction algorithm.
Suppose you are the proud owner of a brand-new coffee machine and you need to maintain it to ensure its proper functioning. Here's how preventive maintenance could be implemented in this scenario:
Regular cleaning of removable parts: You perform regular cleaning of the removable parts of the coffee machine, such as the filter holder, water reservoir, and drip tray.
Periodic descaling: Limescale can build up in the water pipes of your coffee machine. To prevent scale-related issues, you perform regular descaling according to the manufacturer's recommendations.
Checking the condition of seals and hoses: The seals and hoses of your coffee machine should be inspected for signs of wear or leakage. If necessary, you replace them to prevent further problems.
Maintenance of the coffee grinder: If the machine is equipped with a built-in coffee grinder, you clean and adjust it regularly to ensure a uniform grind of the coffee beans.
Now, imagine adding a sensor to your coffee machine to analyze its maintenance. This sensor is installed on the machine and continuously monitors the vibrations generated by the motor during operation:
Real-time data analysis: The data collected by the sensor are continuously analyzed to detect abnormal variations. They allow real-time measurement of the amount of ground coffee, the water pressure during brewing, or the wear of mechanical components, such as the coffee grinder, pump motors, or stirring mechanisms, by detecting signs of imbalance or failure.
Prediction of potential problems: If your sensor detects significant deviations from normal parameters, it can predict potential problems, such as obstructions in the pipes or malfunctions in the heating system, and alert you to significant variations.
Alerts and recommendations: When an anomaly is detected, you receive an alert indicating the potential problem and recommendations on the actions to take.
Proactive maintenance: Based on the alerts, you can plan proactive maintenance, such as cleaning to prevent serious breakdowns from occurring.
The ability to collect and analyze data allows for the identification of potential problems before they cause breakdowns or degrade the quality of the coffee, thus ensuring reliable operation and a quality user experience while avoiding unnecessary part replacement.
These two approaches, preventive maintenance and predictive maintenance, can be successfully applied to keep a coffee machine in perfect working order. Preventive maintenance relies on planned actions, while predictive maintenance uses sensors and real-time analysis to anticipate potential problems and intervene before they occur, just like in your production plant.
The choice between preventive and predictive maintenance depends on various factors, such as the nature of your equipment, your budget, and your expertise in data analysis. Many companies opt for a combination of both approaches, using preventive maintenance for regular operations and predictive maintenance to monitor critical equipment.
It is essential to assess your specific needs and consider the advantages and disadvantages of each method. Preventive maintenance offers a certain peace of mind but can be costly in the long run. Predictive maintenance, on the other hand, can significantly reduce maintenance costs but requires an initial investment often paid off over the long term.
Ultimately, the decision will depend on your priorities and resources. Regardless, effective maintenance management is essential to maximize your company's productivity while minimizing unplanned interruptions.
If you are looking for maintenance solutions, KEYPROD can help you implement the solutions that best meet your needs. Do not hesitate to contact us to learn more about our services and advanced technological solutions!