Predictive maintenance and its positive impact on the environment
New emblem of the industry 4.0, predictive maintenance is a powerful factor of economic performance, but also of environmental performance. Find out how the predictive maintenance market fits brilliantly into the ecological transition.
Predictive maintenance is a modern approach to maintenance, based on anticipating breakdowns using artificial intelligence. In practice, connected sensors are linked to industrial equipment and transmit data relating to their operation in real time. This information is then interpreted by analysis software which, thanks to a machine learning algorithm, is able to identify weak signals indicating the failure or aging of a device. Technicians are then informed by the software of the impending failure and can anticipate and plan the maintenance operations necessary to repair the equipment before it stops working.
Faster and more economical than preventive or corrective maintenance, predictive maintenance is also beneficial for the environment. To what extent? Here are our answers.
Predictive maintenance helps reduce CO2 emissions
When a breakdown occurs and part of a factory’s production line unexpectedly stops working during working hours, the challenge for maintenance services is to act as quickly as possible to limit the loss. Generally, this involves implementing emergency interventions and bringing out heavy artillery, both on a human and on a technical level. To repair a device as quickly as possible, it may be necessary to involve several technicians and mobilize a particularly energy-intensive emergency repair arsenal.
Knowing that an industrial site has an average of 25 unplanned machine stoppages per month, this type of operation inevitably affects the company’s carbon footprint.
Thanks to the predictive maintenance and the regular and strategic maintenance of the equipment in the light of the information provided by the sensors, the occurrence of corrective maintenance interventions is greatly reduced, as well as their environmental repercussions.
Predictive maintenance extends equipment life
Very often, defective industrial equipment that needs to be replaced could have remained functional for a long time with better maintenance. The reason for this inefficient maintenance? A difference between the theoretical wear of an asset and its actual wear, which depends on many factors not taken into account in the manufacturer’s maintenance recommendations. It is therefore common for certain equipment to wear out more quickly than expected or to have malfunctions that go unnoticed until the breakdown occurs.
Predictive maintenance, by detecting the first signs of equipment weakness, allows experts to restore it using often minimal repairs and to avoid premature aging.
For example, it may be a question of sealing a crack before it spreads irreversibly, or replacing a damaged screw before an entire structure collapses.
Optimization of energy consumption thanks to predictive maintenance
It is very common for a malfunction in equipment to lead to overconsumption of energy. In the case of a localized problem on small devices, the impact on the environment can be anecdotal. But when it comes to major infrastructure, the volumes of energy consumed are increased tenfold, and the slightest anomaly can have considerable ecological consequences.
Let’s take the example of an airplane:
- an airplane consumes around 3 liters of fuel per passenger for every 100 kilometers flown
- the smallest Air France plane, occupied at an average rate of 85.7%, carries 111 passengers
- 1 liter of kerosene consumed is equivalent to 3 kg CO2e
As you may have understood, on the scale of the aeronautical sector, even a minimal overconsumption can have serious repercussions on the environment.
Here again, a predictive maintenance solution would make it possible to identify a fault in an aircraft engine before it occurs, and thus prevent excess greenhouse gas emissions. The key is cost reduction, savings in natural resources, and, ultimately, the opportunity for engineers to design optimized parts to reduce the ecological impact of an entire sector of activity.
To learn more, discover the use cases of our predictive maintenance solution in the energy sector.