HomeEnterpriseThree predictive maintenance case studies

Three predictive maintenance case studies

Predictive maintenance brings efficiencies to aircraft, train and elevator operations

As the internet of things (IoT) drives the digital transformation of a variety of industries, predictive maintenance has emerged as a key use case. The ability to use sensor-based monitoring to identify and mitigate mechanical and other problems before they manifest allows industrial interests to avoid downtime and optimize asset performance.

Maintaining the F-35 and F-22

Lockheed Martin turned to 3D technology and big data to streamline the diagnostics and maintenance processes for its F-35 and F-22 fighter planes, according to a case study provided by the Industrial Internet Consortium. The F-35 Lightning II is a family of fifth-generation stealth aircraft developed by Lockheed Martin designed for the nine participating nations in the Joint Strike Fighter (JSF) program, the largest cooperative of its kind since World War II.

Lockheed Martin not only designed and is building the F-35, it also delivers F-35 sustainment support, which includes training, prognostics and maintenance with the goal of helping military customers improve on-the-job support, while prolonging equipment life and maximizing the aircraft’s operational availability.

It goes without saying that the accurate assessment of the mechanical structure of an aircraft after combat or environmental hazard is crucial. Even small factors — such as the depth of a scratch or the distance of a hole from supporting structures — can impact flight-worthiness, aircraft stealth capability and pilot safety. In order to ensure the proper maintenance of all these small details, Lockheed Martin looked to streamline its damage and assessment process for the F-35.

They wanted to move away from the traditional way of doing things, with maintenance technicians manually assessing and tracking damaged areas by placing a transparent film over these areas and tracing reference points, like seems or fasteners, with a marker, and cross-referencing the information with repair data history in a spreadsheet. The problem with cross-referencing line drawings is that it does not provide the optimal platform to visualize repair information, it is also cumbersome and time consuming, leaving more room for maintenance errors, according to Lockheed Martin.

Deutsche Bahn railway operations

Deutsche Bahn aims to implement predictive maintenance tools to be able to control its points.To enable that, the current pulse at the connection cable to the point is measured in the signal box. Special sensors also record status data, which is then analyzed centrally, at relevant parts of the point controller. Possible deviations from the reference value, such as occur before faults arise, are detected by smart systems at an early stage. Once this occurs, service teams can then inspect the affected parts more closely on site and  replace them if necessary so that operations are not disrupted.

The agreement between Telent and Deutsche Bahn comprises system components, planning services, installation and commissioning of the solution, which monitors a total of around 7,000 point machines at 305 operating control points throughout Germany.

“Telent has been a reliable partner to Deutsche Bahn AG in the telecommunications segment for many years and has demonstrated its strength as part of nationwide rollouts, in particular. We’re delighted at continuing our working relationship,” says Dirk Bernhardt, head of purchasing for telecommunications infrastructure, train marshaling yards and equipment at Deutsche Bahn.

Thyssenkrupp optimizes elevator operations

Elevator, escalator and moving walkway manufacturer Thyssenkrupp has been using the predictive maintenance capabilities of Microsoft’s Azure suite, which allows Thyssenkrupp’s service technicians to identify problems with elevators. Combined with the HoloLens “mixed reality” device, technicians can use the industrial IoT tools to work hands-free while on the job, and make remote calls to more experienced technicians who can walk them through solutions – and provide them with valuable on-site education.

“Predicting problems enables us to have fewer service interventions, and this equipment helps us do our job faster,” said Andreas Schierenbeck, CEO of Thyssenkrupp Elevator. “Today our techs have a laptop or cellphone in one hand, or are checking information on laptops. While they’re doing that, they can’t work. But with HoloLens, they can speak with a control room, which guides them to replacing the right components. They can check their work and make sure they don’t have to come back for a second call. They can look at a 3D hologram of parts, explore the system and automatically order what they need. This is important with an urban population that will grow by 3 billion over 3 years. In Manhattan, an elevator is built every day.”

One World Trade Center in New York City is one of many buildings where HoloLens will be used, as the site is already connected via the Microsoft Azure cloud with MAX, Thyssenkrupp’s IoT-enabled predictive maintenance solution.

“Utilizing an out-of-the-box Skype experience without any additional development required, thyssenkrupp’s 24,000 service engineers can now do their jobs safer, and more efficiently, by triaging a service call ahead of the visit,” said Scott Erickson, general manager of Microsoft HoloLens.

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