The elevator industry has yet to become ‘tech-savvy’. However, the multi-million dollar maintenance market could become the potting soil of game-changing innovations. While major manufacturers seem to be groping forward and SMEs are struggling to find the money and time to devote to such innovations, newcomers are advancing rapidly and offering strong innovations: from AI to sensors, many solutions are already available to monitor elevators and even to implement predictive lift maintenance! But in a market so new, and so technical, it is hard to tell which solution is really the revolutionary one that will disrupt the market. Among them, two ways of tracking elevator activities confront each other: adding sensors vs controller data.
The elevator IoT market is buzzing with new ideas but lacks clarity.
Predictive maintenance, which consists of avoiding breakdowns thanks to artificial intelligence, has been taking an increasing place in every technological and industrial field.It allows countless possibilities to track and analyse various data, and leads to knowing which actions to take and when. AI-enabled machines can produce real-time insights and its analytics can be used to make predictive guidance and ensure more transparency for end-customers. Although all the major manufacturers have long deployed connected solutions on their machines, none of them has really pushed the concept of predictive maintenance to its paradigm. The real question is: what is the best way to do it ?
A three-part model rapidly became a norm:
- add sensors and measurement tools to a specific infrastructure to collect raw data,
- create a connected device to transmit the data to the cloud,
- create analytical tools to analyse the raw data and transform it into useful insights for service providers and users.
Every existing elevator is already equipped with basic sensors, commonly used for security and cabin control. For example, built-in sensors are able to detect excessive speeds of travel, to calculate electrical consumption, to align the elevator to the correct floor height, preventing tripping hazards. The innovation of IoT elevators is that they are connected, allowing for real-time performance monitoring from a remote location. Not only the data produced by the machine is collected, but an AI will be able to use it to forecast possible breakdowns and to carry out remote checks to ensure the working condition of the elevator without having to send a technician on site. Every major player of the global elevator market, including the BIG4 companies have begun to introduce predictive maintenance marketing and connected services for field operatives. But they have yet to demonstrate a major commitment to the pursuit of better operational results enabled by predictive maintenance. Thanks to data analysis and failure tracking, elevator service providers should be able to save building owners and maintenance contractors valuable time and labor cost.
Despite this welcomed improvement, we have not yet been able to observe any concrete progress in this area. In addition to this, strong innovations are hard to follow, partly due to piecemeal adoption across the industry. There is a kind of escalation in technological innovation, with manufacturers who market exaggerated technological solutions, without demonstrating any concrete operational contributions. From basic detection systems, commonly found on last-generation elevators (accelerometer, laser, door sensors) to downright eccentricities such as biometric access control solutions or tools allowing a better passenger repartition in the cabin, the elevator IoT market has become a “catch-all”. It feels like some service providers kind of lost sight of what we believe to be the sole objective of elevator service : effective and transparent maintenance. Let’s admit it, we often saw a lot of marketing and a lot less of tangible innovations.
One last observation: some technological proposals appeared to be redundant. Indeed, the control card of almost all elevators on the market - as well as the security chain of the latter - already provide enough usable data for the implementation of IoT!
Here’s all the data already collected by most of elevator controllers:
Elevators already produce loads of data: we choose to use it rather than adding superfluous new data points.
At uptime, instead of adding physical sensors to the elevators, we choose to rely on those that already exist, and which are accessible through the control card, which is the brain of the elevator. 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.
Today, our in-house predictive technology, is able to translate and process all the operating data produced by an elevator, which is captured by our hardware. Thanks to a long and meticulous R&D process, we managed to normalise field data reporting, with a brand-agnostic structure. Early in the construction of our technology, we came to the conclusion that the controller data is more insightful and useful than anything we could get from third-parties installed outside of the existing hardware. As we saw, most elevators already have tens of trackers installed. Finding a way to use them properly and orderly turned out to be the best solution.
Today, technicians using our technology can choose from 8 actions on more than 200 normalised components, all of it single-handled by our controller-connected device.
IoT models comparison :
Sensors shouldn’t be the main focus when so much information is already available and ready to use!
We strongly believe that sensors are not a priority but rather a secondary equipment that can be used to maximise performance:
■ We already have enough data to provide a top of the art maintenance monitoring and predictivity. At uptime, we’re already able to collect and process more than 7000 events a day, on any elevator. We’re able to track the elevator’s health through 3 different levels (continuous data flow and specific statuses & error codes through remote control), without a single sensor being added to the machine!
■ Sensors often duplicate data that is already available through the controller. Sensors can add value, but most of the information that can be provided by external sensors are already available directly from the controller, which adds more redundancy.
■ Sensors are expensive and need specific knowledge! Not only installing third-party sensors on an elevator can be very expensive if it is to collect truly useful additional data, but the technicians will also need additional knowledge to use it and maintain it properly. Even more constraining, specific development will be needed to properly add it to the existing technological stack.
■ Sensors are sensibles and subject to defaults! For example, cold or dust can impact and deteriorate external sensors, such as one installed inside the shaft.
Differences between a sensors-based IoT and a controller-based IoT like ours:
In some cases, secondary sensors attached to the cabine and in the shaft can bring a wider variety of elevator health measurements such as temperature, noise, vibration, humidity, speed and acceleration in addition to providing a higher level of sensitivity and accuracy. But most of these can already be tracked by exploiting the data provided by the controller. At uptime, this is what we believe in. Thanks to our unique IoT, directly connected to the controller, we generate hundreds of predictive alerts and maintenance recommendations, thanks to our analysis algorithms. In addition to that, our technology is brand-agnostic, therefore open to any maintenance company in the world. Learn more on the link below.