The infrastructure of contemporary buildings has substantially changed over the last two decades and has become increasingly more complex. Supermarkets, shopping malls, office towers, business centers, conference halls, hotels, hospitals and many other types of public buildings are now equipped with a variety of devices that support building functionality. Elevators, escalators, lighting equipment, automatic and fire doors, fire alarms, and other kinds of building systems, require systematic maintenance programs in order to ensure the dependability and availability of services, as well as the safety of building users. Maintaining building functionality poses new challenges for facility management, especially in relation to monitoring and maintaining components.
Internal and external building environments generate large quantities of data, such as temperature fluctuations, power and water consumption, parking place availability and access to various areas within the building, all of which must be thoroughly analyzed and shared among stakeholders.
To ensure successful information gathering and to create an informed foundation for decision-making, management should employ advanced strategies based on the latest information and communication technologies. Today, the demand for intelligent and predictive facility management is on the rise; there is an urgent need for predictive maintenance and condition monitoring of equipment, as well as predictive security and energy management. These processes are all essential components of the modern interdisciplinary field known as Intelligent Facility Management (IFM), an approach based on collecting data about key building parameters (both current and historical) and providing real-time analysis of the collected data to support informed administrative decisions.
With advanced data collection systems, enhanced capabilities for data storage and transfer, and sophisticated analytical tools, IFM provides an effective means of controlling assets and operational processes. IFM supports optimization of energy and water consumption, establishment of sensible maintenance scheduling, increased device availability and reduction of outages. Moreover, the availability of cloud solutions for asset monitoring, which can be used in conjunction with this approach, make it possible to detect probable risks, find the root causes of problems, sort essentially different data and ultimately predict and ideally prevent potential future failures or resource losses.
A good example of successful IFM is the collaboration of a major elevator manufacturer with a number of IT service providers to provide effective monitoring of its elevators. In order to control different elevator functions—such cab capacity, cab speed and cab door opening—a building’s elevators can all be connected to the cloud. With the use of specifically developed software, the vendor now has a novel maintenance system capable of supplying real-time information on the condition of its elevators, enabling a preventive maintenance approach to be implemented in their upkeep. The core concept—creating cyber-physical systems that combine mechanical and electronic elements using powerful IT solutions— has proven so successful that the group is now launching it in other areas.
Several features of IFM make it particularly effective:
Organization of Data Flow
The implementation and success of IFM is entirely dependent on data; as such, the means by which data flow is organized becomes critical. Data is drawn from two different sources: from on-board computers and sensing elements, and from engineers, technicians, and other service/maintenance staff. Sensor data and field staff feedback supplement each other and are combined into an integral data set, which forms a comprehensive information base for decision making. Data is continuously updated so that maintenance personnel can keep track of the state of equipment on an on-going basis.
Superior Ability to Predict Future Problems
IFM is based on probability and statistics theory. Comprehensive data analysis provides information about building trends and the causes of system and equipment failures, and enables preventive planning to mitigate and avoid the occurrence of breakdowns before they happen. In addition, a key element of the IFM approach involves striving to understand the mechanisms involved in component and system failures. Developing an understanding of these mechanisms requires knowledge of the inputs involved in failure: the occurrence frequency of each problem, the combination of possible causes that can lead to failure, and analysis of what happens if a failure takes place. The goal of IFM is to reduce the impact of these inputs or completely eliminate them. The criticality of a failure is normally what determines the priority of efforts. All of these measures allow managers and engineers to anticipate potential problems.
The same is true for monitoring energy and water consumption, which supports the effective future scheduling and distribution of resources.
Preventative maintenance is much less costly than servicing major breakdowns; therefore considerable savings may be realized on the cost of parts and labor using the IFM approach.
Apart from savings in these areas, IFM also offers an automated ability to determine the most cost-effective window in which to replace a particular piece of equipment rather than continuing to incur the high maintenance costs necessary to service it. IFM tools can help determine the point at which continued operation and maintenance costs exceed replacement cost, and predict the optimum time for device replacement.
Moreover, focusing on HVAC controls and automation allows for the most economically reasonable distribution of resources within a building. Using boiler/pump stops and adjustable shutdown timers ensures effective scheduling, minimizes unnecessary uptime of facility equipment and prevents excessive power/water consumption.
Actionable operational tools
The IFM approach has at its disposal a range of highly effective operational tools. These tools include:
- Predictive condition monitoring – a strategy for monitoring equipment condition parameters (e.g. temperature, friction and vibration) to identify signs of a potential fault, and
- Energy prediction – a statistical approach to analyzing the local dynamic properties of a system to arrive at optimal energy/water consumption and distribution schedules. The data, obtained from monitoring activities, allows managers to model the consumption of resources under alternate scenarios. In addition to this, with its extensive information gathering capacity, IFM has great potential for increasing the reliability of security and safety installations.
IFM has been shown to substantially improve the overall operational, cost and safety features of modern-day building infrastructure. Utilizing the capabilities of computer systems and software, IFM makes facility operation more productive and far less costly by decreasing energy consumption and increasing the overall sustainability, resistance to failure and ROI of building equipment and systems.