Over the last several years data generated by aircraft has increased exponentially thru introduction of new onboard equipment that allows for the so-called connected aircraft. In the wake of this data growth OEM’s and airlines alike the interest in data analytics and predictive maintenance has risen alike. According to the MRO survey 2017 by Oliver Wyman (When Growth Outpaces Capacity) 77% of respondents want to implement predictive analytics in the coming three years. The MRO survey 2017 by Aircraft IT confirms this trend as most respondents answered that next to paperless maintenance predictive analytics will play a major role in the development the upcoming years. According to the latest MRO Survey by Oliver Wyman (2018 – Tackling Industry Disruption) predictive maintenance is even seen among airlines as a means to combat rising material costs and improve labour productivity and shortage in the future.
The main question you have to ask yourself: 'Where are we currently standing: is our airline ready for predictive maintenance?'
Take the free questionnaire to discover at which stage your airline stands and receive feedback what you can do next. You will receive a detailed feedback report within 2 working days after you submitted the questionnaire.
The aim of predictive maintenance
Let’s start at the beginning what is the aim of predictive maintenance?
Within aviation maintenance and engineering the aim of predictive maintenance is first to predict when component failure might occur, and secondly, to prevent the occurrence of the failure by performing maintenance. Monitoring for future failure allows maintenance to be planned before the failure occurs, thus reduce unscheduled removals and avoid AOG
Benefits of applying predictive maintenance
- forecast inventory
- manage resources
- minimizing the time, the equipment is being maintained
- minimizing the production hours lost to maintenance, and
- minimizing the cost of spare parts and supplies.
Based on years of in-depth research EXSYN has identified three main phases, where the last defines the status of being able to become predictive:
Let’s have a closer look at the three types of airline:
The reporting airline
The reporting airline is characterized by producing monthly reliability reports, drawing from manual work of retrieving the various information such as aircraft utilization, Pilot reports, Maintenance findings and component removals and then consolidate this data into Excel.
This typically is a very manual labour-intensive process and puts the reporting airline in a situation that they are able to produce the reports on monthly bases but not action on these reports.
The monitoring airline
The monitoring airline, stands one level higher on the adoption success scale of becoming predictive. This type of airline typically already moved away from excel reports and uses an analytical tool and mostly also dashboards. This allows the airline to act more quickly on rising issues and monitor the fleet than rather reporting the status of their fleet.
However, it still draws on the same set of data, the SPEC2000 chapter 11 reliability data. Which ultimately prevents the airline of adopting any predictive maintenance models as data sets are to limit to become reliable enough for operational usage.
The data driven airline
The data driven airline, has the highest level of adoption success for predictive maintenance models. It used all character traits of the monitoring airline but has recognized the need of having access to larger sets of data beyond its own airline data, such as industry reliability data, Flight Data Recorder information, weather data as well as ADS-B transponder data. Ultimately the data driven airline implements a data driven platform in its organization that provides information to its operational units such as maintenance operations control to make informed decisions in day-of-operation itself.
Do you would like to receive more detailed advise on how predictive maintenance can increase mainetance efficiency and reduce costs in your organisation?
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