The MRO and aviation maintenance industry at large is constantly being overhauled by advancements in technology. Predictive maintenance is one of the emerging technologies poised to reshape the future of aircraft maintenance.
Lufthansa Technik has been a pioneer of predictive maintenance field since its launch of AVIATAR in 2017. AVIATAR was born to utilize the data of modern aircraft. With AVIATAR, data can be used to better monitor the health of a fleet and maintenance can be tackled before it becomes a problem.
Mia Witzig, Head of Digital Solutions Predict & Recommend at Lufthansa Technik, joins AMT to discuss AVIATAR and how the field of predictive maintenance is advancing.
AMT: What is predictive maintenance and how does it differ from traditional maintenance practices?
With the aim of reducing operational interruption caused by technical problems and decreasing the costs of unforeseen maintenance events, Lufthansa Technik provides Predictive Health Analytics (PHA) on the AVIATAR digital platform alongside other digital solutions like Condition Monitoring and Reliability Management. The wide-ranging plugins automatically predict failures of aircraft components and systems altogether with recommend maintenance measures to prevent them. The solutions are available for Airbus, Boeing and other aircraft types operated by commercial airlines.
In addition to a fixed maintenance schedule, predictive maintenance relies on data analytics and machine learning algorithms combined with our extensive technical expertise to predict when maintenance tasks are required. By analyzing real-time data, performance trends and failure patterns, operators and MROs can plan maintenance activities when, for example, the condition of an Integrated Drive Generator (IDG)on an engine requires intervention based on live data from the aircraft. This reduces MRO costs, minimizes unexpected failures and optimizes operational efficiency as well as time-on-wing compared to traditional approaches.
The PHA solutions on AVIATAR constantly monitor real-time data and immediately alert the airline's technical operations staff when technical actions are required. By taking into account an extensive base of statistical data as well as aircraft engineering and component overhaul experience, the solutions can anticipate failures before they occur and could be used to optimize the required maintenance schedule. This avoids unexpected removals and unplanned aircraft downtime.
AMT: What are the primary benefits of adopting predictive maintenance?
Combining latest big data analytics technologies, advanced data science and years of experience in aircraft operations, engineering and MRO, Lufthansa Technik’s AVIATAR helps minimize ground time and costs by predicting and reducing operational interruptions (OI) with Predictive Health Analytics. By minimizing OI and increasing availability, unscheduled events are transformed into planned maintenance events for aircraft. By generating recommendations and being able to forward them directly to the airline's maintenance system like AMOS or Trax via AVIATAR, significant cost reductions can be achieved across the entire technical operations.
See, for example, finding the optimum time to replace or service the IDG: Our digital PHA solution monitors a variety of digital parameters within the customer’s aircraft data in order to anticipate IDG failures in real-time. Once the algorithms predict a potential system failure, the Predictor Plugin automatically generates recommendations and alerts that can result in automatic work orders in the customer’s maintenance system.
AMT: What are the key steps involved in integrating predictive maintenance into existing operations?
The first thing to do for airline representatives is to contact the AVIATAR team to analyze the aircraft types and configurations in the operator’s fleet. After a quick capability and configuration check, an agreement needs to be signed. This ensures protection of data and intellectual properties as well as a definition of interfaces for the flow and definition of data required for the selected solutions. Once these standard interfaces are connected, an operator can use the cloud-based solution right away via a web interface.
AMT: What technology and data analysis tools are essential for implementing an effective predictive maintenance strategy? What kind of data should be focused on?
AVIATAR Predictive Health Analytics Plugins analyze aircraft data. They incorporate reports from aircraft condition monitoring systems and quick or digital access recorders (QAR/DAR) for example. Additional sources including maintenance and engineering information can be used to enrich analysis. Proven model-based algorithms recognize, calculate and visualize trends in performance data to predict future removal, maintenance and repair requirements of various aircraft systems. The digital tools also predict the remaining time before maintenance action is required. It can be connected to the customer’s maintenance system to automatically trigger servicing measures.
AMT: What are some common challenges organizations face when integrating predictive maintenance and how can they be overcome?
The digital transformation of Technical Operations is not completed by downloading a few apps. For sure, it requires leading digital solutions, but it also requires an organizational transformation, if an airline wants to create value. This is probably the biggest hurdle, but AVIATAR has a proven track record of cooperation with operators to overcome it. Collaborating with our customers, we can see that the gap between digital leaders and followers is starting to grow. With digitalization being the only game changer in this decade, it stands to expect that it is starting to make a significant competitive difference already.
AMT: From a cost perspective, what are the benefits of predictive maintenance?
Nothing is changing the MRO industry and is driving the development of new solutions more than digitalization. In this decade, it stands out as the only game changer. With 50x more data generated by new aircraft types and approximately half of airline operating costs consisting directly or indirectly of MRO services - the potential for cost reduction lies in optimizing MRO and operations driven by digitalization. No further major cost savings potentials are expected before the next aircraft generation with new physical technologies will be introduced after 2030. For a larger fleet of aircraft, some predictors can easily create six-digit savings for certain operators.
AMT: What training or skills are necessary for maintenance teams to use predictive maintenance and get the most out of it?
There is no special training necessary to implement PHA into our partner’s and customer’s operation. After an implementation phase supported by AVIATAR experts, the engineering and troubleshooting teams in the Maintenance Control Center (MCC) can access analyzed and enriched data from their aircraft in real time from any device. They are provided with proactive and reliable recommendations built on data analytics and can therefore recognize trends before a failure occurs or distribute them directly to the airline’s maintenance system via the AVIATAR platform to create the corresponding work order.
AMT: Where do you see the future of predictive maintenance heading, and how is Lufthansa Technik preparing for these developments?
Key is growing the number of predictors and applicable aircraft types based on market and customer requirements. Together with the other applications on AVIATAR and of its partners in the Digital Tech Ops Ecosystem, AMOS and flydocs, Lufthansa Technik offers an unprecedented digital coverage of the tech-ops value stream using a customer-centric and collaborative approach.
AMT: Can you provide any specific case studies or examples where predictive maintenance significantly improved aircraft maintenance or operational efficiency?
As mentioned before, the Integrated Drive Generator (IDG) is a great example how our algorithms predict a potential system failure and the Predictor Plugin automatically generates recommendations and alerts that can result in work orders in the customer’s maintenance system.
Overall, some customers estimated that through over 1,100 yearly alerts of AVIATAR’s PHA and resulting less AOGs, saved time and resources, elimination of ineffective actions as well as a better passenger experience, they saved about 26,000€ for one Airbus A320 per year. Since such numbers are confidential, we cannot share the names of the customers publicly.
