The new Holy Grail in building services will be guaranteeing uptime. It’s looking increasingly possible thanks to the Internet of Things (IoT) and advances in artificial intelligence, according to Greg Hill, Senior Business Analyst at Joblogic.
Combining connected devices with automated systems means you can now gather data, analyse it and create actions. Just a decade ago this would have been passed off as something out of Aldous Huxley’s Brave New World, but disruptive technologies are changing how we use data and act on it.
IoT will enhance automation, enable efficient data collection and ensure fast and effective analytics. It will optimise workflows, streamline processes, and most importantly, enable you to predict and plan maintenance with precision.
Fixing things before they break still comes with an air of futurism. But thanks to IoT, predictive maintenance is evolving at a rapid pace. It means that we will soon be able to guarantee building services uptime.
The evolution of predictive maintenance
In the past, building maintenance operated largely by booking in a service for equipment such as air-conditioning or ventilation. While much preventive work gets carried out in time, unplanned emergency repairs are still essential.
Known as ‘time-based maintenance’, the servicing frequencies are based on warranties, supplier instructions, legislation or experience. For example, the annual servicing of an air-conditioning unit, or six-monthly cleaning and/or changing of filters.
But time-based maintenance can come too late, resulting in more extensive repairs than may have been necessary if a progressive problem had been picked up sooner.
Extensive real-time monitoring, data collection and analytics brings a wealth of extra information to the table. It means small issues are picked up sooner, common problems analysed, and preventive maintenance planned before wear and tear or breakdowns occur. IoT enables far greater precision.
This, known as ‘condition-based monitoring’, is a far more efficient way of servicing, compared to arbitrary time-based maintenance. IoT means that suppliers can be far more confident about guaranteeing uptime. And businesses needn’t be worried about how to incorporate IoT into their operational models – most equipment today is already smart or can easily be retrofitted with sensors to make them smart.
Predictive maintenance will change in four key areas:
● Using Artificial Intelligence
● Real-time data
● Performance metrics
● Responsiveness and timely repairs
Artificial Intelligence
Artificial intelligence (AI) will transform diagnostics by factoring in behaviour. For example – if a freezer in a restaurant or a supermarket is opened regularly at certain times of the day, temperature drops during these times will be ignored, but if it happened in the middle of the night, it would be flagged as a possible compression failure and automatically call an engineer. Rather than waiting for a system to fail, facilities managers and suppliers will be able to use AI to identify pre-failure patterns.
Real-time data
Real-time data will save significant time in diagnostics. Pre-IoT, when an engineer is called to fix an equipment issue, they may not fix it immediately. They might need to pick up a part, order it or they may even need to replace the equipment and schedule another visit.
IoT provides real-time data so engineers are ready to make repairs. If it is integrated with job management software, engineers can also seamlessly swap jobs to suit expertise or the parts they have.
Performance data
The success of IoT performance monitoring depends on deep visibility and insights into the data collected by sensors. An intelligent analytics layer will make sense of this collected data.
Performance data can determine whether maintenance jobs are urgent or can be scheduled further in advance.
Responsiveness and timely repairs
IoT will accelerate responsiveness massively. Sensors will identify abnormal patterns, so building maintenance can be triggered automatically to intervene.
For example, if a building has 10 air-conditioning units, there may be an annual servicing policy to replace all the filters. Some may be replaced unnecessarily, while others may be beyond their best. Or if the filters are only replaced when a unit breaks down, there are huge repair costs at stake. IoT makes it possible to plan optimum, automatic repair schedules for individual units.
Why business models must change
Presently, most building services firms provide service packages based on a set number of visits to service specific assets each year. IoT, specifically with its impact on the precision of predictive maintenance, offers the possibility of moving towards an insurance-based package, where customers pay to keep assets running for an acceptable uptime.
Prior to IoT, this would have been hugely expensive for customers, but with the preciseness and predictability IoT brings to the table, maintenance companies can make such packages cost effective to customers.
Sensor data from IoT devices will optimise maintenance activities and, over time, reduce costs. It means firms won’t carry out maintenance unnecessarily, or too early or too late, and will be able to make forecasts about deterioration.
Integration of systems for the future
Business models will need to shift according to the different levels of IoT architecture they adopt. Integration of systems will become increasingly valuable in achieving optimum operational efficiencies.
Digital synchronicity is vital – the aim is to detect degradation of equipment as early as possible and carry out maintenance quickly. First, data must be collected in real time. Then appropriate tools must process this, and take the maintenance history, operational data, design and application into account. The next step is to integrate with the system that orders parts and schedules an engineer.
This is where integration with field service maintenance software completes the loop.
If the software has an API (application programming interface), it is ready to integrate with third-party data sources. It means that data from sensors is collected and analysed outside of the field management software but can communicate with it via an API to generate actions. If, for example, sensor data determines that a building is too warm, the AI engine can automatically communicate with the field management software, which then uses the information to create a job and schedule an engineer. This can all happen digitally without any calls.
The real-time data and synchronicity between sensors, databases and systems mean that engineers’ jobs in the future will largely revolve around preventive fixing. Complete breakdowns will become a rarity. Replacements will be diagnosed and scheduled offsite.
Remote diagnostics and scheduling can even happen during the night, so in the case of workplaces, for example, a problem can be fixed before the start of the working day.
The world of building maintenance is set to change. IoT will provide the intelligence but it is up to building services suppliers to adapt their model. Don’t be surprised if guaranteeing building services uptime becomes the new benchmark.
