The map® is uptime's complete technology system. Its strength ? Being able to process, analyze and exploit all the data created on a daily basis by your operational, customer support and sales teams tools. At the heart of the map® is the desire to provide any elevator maintenance company a means of increasing the quality of service, for the benefit of your customers. The key for us lies in the development of a predictive elevator maintenance system strong enough to bring us all the knowledge needed to build the perfect technology.
What is predictive maintenance ?
When we talk about the maintenance of a physical system (which can therefore deteriorate over time the multiple hazards of a daily use), there are generally two main approaches:
■ The reactive approach: It consists of only intervening on the system when it crashes. When it happens, all we can do is to repair or replace the defective component(s). This approach can be less expensive, only if the cost of a failure is less than the means that would have to be put in place to avoid it. Beyond that, it is for sure the approach that causes the most downtime.
■ The preventive approach: It consists of intervening more or less regularly, to carry out repairs and checks on the system, hoping that this will prevent it from breaking down. This approach therefore requires more resources, depending on the frequency and intensity of your interventions.
For the preventive approach, we can distinguish several ways of making these maintenance visits: regularly, according to a fixed schedule (as it is the case in the the elevator market), episodically, according to criteria of use (such as the change of belt every 130.000 km for a car), or depending on more complex conditions correlated with component wear - this is where predictive maintenance makes its entrance.
We can apply two approaches in parallel to the elevator:
■ To calculate the criteria of use leading to a repair. In the automotive industry, or in most industrial sectors, the components of a system have known lifetimes (typically according to number of cycles) which make it possible to determine replacement criteria. But this is not the case for elevators. With sufficient data on the faults found in a fleet of lifts, it is however possible to deduce such criteria, depending in particular on the traffic. For example, it is possible to determine how often to replace car door rollers for an automatic door elevator if there is sufficient history of the failure of that component.
■ It is possible to extend this principle to more complex criteria, to modulate the frequency and content of preventive interventions. Thanks to algorithms based on physical measurements, system characteristics, and a correlation with a failure history, we can determine preventive actions to be implemented to minimize the downtime of the system, while avoiding as much as possible the associated additional costs (often linked to too frequent or too premature preventive actions). This means that the technician can be guided through his maintenance visit by providing adjustment and replacement recommendations determined from observation of the behavior of the system.
What does it mean for the end customer ?
Take the case of a standard elevator. Today, it has to be serviced every 6 weeks, but visits are often limited to security checks that have no real value to avoid breakdowns. Tomorrow, with the map®, the technician can be informed that the door rollers on the 5th floor are probably damaged. He will be able to prepare for his visit by having the part with him at the time of the visit, and focus his attention specifically on this door, to replace the rollers if they are indeed nearing the end of their life. Thus, he will have avoided a breakdown in the elevator, but will not have replaced this part until the necessary time.
Compare between reactive, preventive or predictive maintenance:
At uptime, we understood that the development of such a system could only go through a very strong integration between all the teams working around the elevator: from the R&D engineer to the technician, including the sales and the customer support manager. Thus, the creation of the map® requires the conjunction of several factors:
■ Expert technicians, able to provide their business knowledge, and provide information on the levels of wear observed in the field
■ Sales and customer support teams closest to the needs of our customers and elevator
■ Users, allowing us to anticipate the potential impacts of each new development
a high-performance IoT system, capable of collecting and transferring information in real time on the operating status of the elevator
The key, of course, is to make these three components interconnected. A major obstacle to the development of such a system among more traditional players is their operation in silos. At uptime, even if the technical, sales, customer support and R&D teams are distinct, we have created a real synergy between them. Thanks to daily contacts (for example, technicians are integrated every week in the development of new functionalities) and mechanisms encouraging cross-functional operation (the OKR framework for example), everyone's expertise is combined to develop the map.
Our technical stack
Today, the map® is the embodiment of these 3 factors. It includes two business applications for our operational team: Field, the companion mobile application for the technicians, which allows them to manage their day, but also to obtain information from the unit, and to report valuable data on their actions, and HBO, the web tool that allows our operational managers to manage the entire uptime fleet. On the client side, we have developed the uptime Connect platform, making it possible to transmit all the necessary information on the fleet under management at uptime. Finally, the operating data is captured by our ELAN box developed in-house.
Precisely, this case is the key that allows us to provide real value in the field. Contrary to the approach intuitively taken by several players in the market, we have chosen not to add physical sensors to the elevators that we maintain, but rather to rely on those that already exist, and which are accessible through the control card (the "brain") of the elevator. The advantage is that this data is much more standardized, less dependent on the physical differences between the installations, and much easier to capture (installing sensors on an elevator can be very expensive if you want interesting data). The downside, of course, is that retrieving this data requires being able to understand each control card from each manufacturer - so we have developed expertise in this regard.
On the algorithmic side, we have developed the necessary tools, allowing us:
■ to visualize and analyze operational data, by crossing our field data (from Field), IoT (ELAN) and customer (Salesforce), all on Metabase.
■ to develop algorithms for predictive maintenance (and improvement of remedial action, such as failure detection), to put them into production and to easily measure them.
uptime is now available to all elevator maintenance companies. We provide a plug-and-play and agnostic IoT to help you make your operations more efficient and to reduce churn by becoming 100% transparent with your customers! 👇