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Mix predictive maintenance with IoT and rule public transport

When you take a train, you expect it to drop you off on time and without being squashed like a sardine in a can. This doesn’t sound like very much to ask, yet it is. The public transport sector struggles to have its trains, buses and ferries run on time, and prevent breakdowns and accidents while keeping travelers well informed and satisfied. Many of these problems could be avoided by better maintenance. Sadly, maintenance done right takes time and money, and maintenance done wrong takes more time, more money and sometimes it even takes lives. If only there was a way to tailor your reparations and speed up downtime. Oh wait. There is. Welcome to the era of the internet of things.

Standard checklist

Public transport vehicles are taken off the road for maintenance regularly. This downtime is crucial, as deterioration such as worn wheels and bad brakes can cause delays or even fatal accidents. To avoid any risks, vehicles are inspected every couple of months, even if there’s nothing wrong. Mechanics check them using a standard checklist, clear them for the road and send them on their way. This predictive maintenance method has two major disadvantages. First, lots of time and money is spent on the maintenance of trains and buses that don’t need it. Second, a standard checklist is not always sufficient, as public transport vehicles are subject to many different circumstances. They differ in rides per day, occupancy level and the kind of service they provide. City buses, for example, will show different wear and tear compared to long-distance buses. Moreover, weather conditions have a large impact on the state of vehicles and can differ per region. Regularly scheduled maintenance may help public transport companies in fixing the larger part of the defects; it won’t help them improve their services.

Don’t predict the state of the engines, know the state of the engines.

The internet way of fixing things

There’s nothing wrong with predicted maintenance; there’s something wrong with the way people make the predictions. Today, most decisions in public transport maintenance are made based on earlier experiences and data. But as I pointed out, there’s just too much variation in the way public transport vehicles are being used. If you really want to know about the state of your buses, trains or whatever it is you’re driving, you should look at your data in real time. Don’t predict the state of the engines, know the state of the engines. With this information, you can schedule tailor-made maintenance for the vehicles that need it and leave the rest alone. How do you do it? You simply do what everybody else does when there’s a problem: you include the internet. By attaching sensors to the different parts of your vehicles (think engines, brakes, batteries), you can request information about them wherever and whenever you want. APIs will gather the input and translate it into workable data that you can use to set in motion the follow up. To do so, you determine thresholds per vehicle part, so that when a parameter goes out of the normal range, the vehicle is taken of the road for maintenance.

The long list of benefits

There’s more to this new way of predictive maintenance than you might think. First of all, tailoring your services based on real-time data will help you save costs. When mechanists know what needs to be done beforehand, they can get rid of the standard checklist and move on to the actual defects. This will shorten the downtime of the bus or train and get it back on the road faster. Second, it will improve the reliability of the vehicle thanks to better targeted maintenance and real-time insights. From now on, you will know about the low tire pressure before the tire deflates, which results in less unplanned stops and unpredicted downtime. Another advantage is that you don’t need as many mechanists and parts as you used to because you only need them when there’s an actual defect or crossed threshold. Lastly, you can optimize the way you communicate with travelers. When you use the internet to monitor the state of your vehicles and to schedule maintenance, you can also use it to inform people on available buses, trains and where they can find them.

IT makes it happen

What does internet-based predictive maintenance look like in real life? I personally like the story of Trenitalia. It uses the internet of things, sensors and data analyses to keep its trains in top shape, and was able to shorten downtime, cut costs and make better predictions. It is one of many success stories, and it proves that we’re close to completely changing the public transport sector. And it needs it, as there’s no other sector in which more people go from one place to the other. They want to be transported in the safest and fastest way possible, while having as much information on their journey as possible. I truly believe that IT can make that happen. Switching to such a digital strategy requires a lot of thought and strategy, though. If you want to disrupt your sector by staying one (or two, maybe three) steps ahead of everyone else, you need technology, knowledge and, most importantly, a cultural switch within your organization. Take all that, mix it with IoT and a great (data) infrastructure, and you will rule public transport.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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