The concept of smart building is now a reality, revolutionising building management. One of the most concrete advantages is undoubtedly predictive maintenance. Here is how it works and how it is applied.
In an increasingly connected and technological world, the Internet of Things, or IoT, is pervasively entering every sector. The building industry is no exception: here, buildings are becoming increasingly intelligent through the use of sensors, IoT devices and advanced management software. These solutions no longer involve the individual dwelling or unit, but structures as a whole. Buildings constructed or modified according to these criteria, called smart buildings, can monitor the indoor environment, user activities and the operation of systems in real time, offering numerous advantages in terms of energy efficiency, safety and comfort.
What does Smart Building mean?
When a new definition reaches the market, there is always the risk that different and not always accurate meanings are attached to it. Fortunately, there is an official definition from the European Commission, which has developed an indicator called the Smart Readiness Indicator that measures the ability of a building to be or become smart. According to this indicator, the key requirements lie in the ability to “sense, interpret, communicate and actively respond in an efficient manner to changing conditions”. This is related to both internal and external factors.
The indications of the European Commission mainly refer to energy efficiency but this is not the only economic and immediate benefit of an intelligent building. Thanks to its network of sensors and devices and the ability to process them, Predictive Maintenance can be applied. Let us see in more detail what this is all about.
Intervene before the fault occurs
Predictive maintenance helps to prevent equipment failures and reduce downtime. But how? Taking advantage, of course, of the features of smart buildings. Predictive maintenance, in fact, consists of using advanced sensors and algorithms to monitor the health of systems and predict when a failure might occur.
For example, sensors on lift motors can detect the wear and tear on parts and their tendency to deteriorate, alerting the maintenance manager when it is time for an overhaul. In this way, maintenance is carried out when needed, on the one hand avoiding the risk of sudden breakdowns and on the other hand the costs that traditional preventive or scheduled maintenance typically introduces. Not all components used have an exactly identical life cycle. In some cases, damage may occur earlier and, conversely, in others, a planned intervention may act on a component that is still functional. Predictive maintenance makes it possible to determine when to intervene based on data and readings, with replacements and repairs carried out exactly when they are needed, always within safety margins.
Not just maintenance, but all-round comfort
Predictive maintenance, however, not only reduces maintenance costs but can also improve the safety and comfort of building users. For example, through the readings of humidity and temperature sensors, it is possible to predict and prevent the formation of mould or damp spots and report the problem to maintenance before it occurs, ensuring a healthy environment for building occupants.
The introduction of environmental sensors, especially on a large scale, also allows more efficient regulation of temperature and the resulting comfort as well as less wear and tear on the components involved. Technologies such as TPI (Time Proportional and Integral control), for example allow consumption to be reduced through more continuous and measured temperature control. The same criterion can be applied to any type of residential automation. For example, thanks to the combination of environmental sensors and data from external sources, it is possible to prevent awnings from being positioned at the pre-set time if rain, wind or other weather events are expected to arrive shortly thereafter. The work thus saved contributes to increasing the durability of the components involved.
Data is a valuable asset, also for maintenance
Smart buildings can also use the analysis of collected data and possible comparison with existing time series to identify problems that tend to recur and improve maintenance processes over time. For example, if a certain plant is particularly prone to a specific type of failure, data analysis can help identify the root cause of the problem and find preventive solutions.
In conclusion, predictive maintenance is an important and characteristic benefit of smart buildings, which can improve the energy efficiency, safety and comfort of building occupants. Thanks to the use of sensors, advanced algorithms and professional service, maintenance can be carried out only when necessary, reducing costs and improving the quality of life of the people living there.
