Predictive Maintenance: From Reporting to Data-Driven Airline
Over the last several years data generated by aircraft has increased exponentially through the introduction of new onboard equipment that allows for the so-called connected aircraft. In the wake of this data growth, OEMs and airlines have shown increased interest in data analytics and predictive maintenance.
According to the MRO survey, 2017 by Oliver Wyman (When Growth Outpaces Capacity) 77% of respondents want to implement predictive maintenance 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 maintenance will play a major role in the development of the upcoming years
Before we continue, let’s first of all answer the question what is predictive maintenance?
Predictive maintenance involves the use of information such as sensor data, and statistical modeling to calculate what the condition of systems and components could be at a given moment in time to predict maintenance needs in advance. It helps airlines to determine when maintenance should be performed.
With predictive maintenance, the idea is to perform condition-based maintenance; so, rather than doing maintenance based on a certain fixed number of flight hours, flight cycles, or calendar dates, we determine which condition of aircraft systems and components would justify performing maintenance actions. In short, reactive maintenance is too late as in AOG (aircraft on ground) situations; preventive maintenance means that you’re doing some jobs too early; but predictive maintenance tries to do maintenance right on time.
Is your airline ready for implementing predictive maintenance?
Based on years of working with airlines and aircraft data, within EXSYN we characterize three different types of airline:
Reporting airline
Monitoring airline
Data driven airline
Each of these three types of airlines stand on different success levels for adoption of predictive maintenance.
Success levels to predictive maintenance
Lets 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 1 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.
Any airline wanting to make use of the benefits that predictive maintenance brings to its operation will first need to start with the evaluation of which type of airline they are today. The outcome of this question will determine the roadmap that will need to be laid down to get to the point of highest success adoption of being able to implement predictive maintenance principles in the airline business.
The question is where are you currently standing, are you ready for predictive maintenance?
Here are several questions you should to ask yourself:
Has your airline an integrated MRO software?
Is your MRO software less than 8 years old?
Is the MRO software updated at least once a year?
Does your airline have interfaces built between various sources of information within your IT setup? (i.e. Records, Maintenance, Engineering, Inventory, Finance & Resource, Flight Operations data)
Does your airline conduct data audits to secure that data is up-to-date?
Does your airline have a high data integrity/quality?
Do you cross-reference and update data against sources and revisions?
Does your airline use a software solution (not EXCEL) to perform reliability analysis?
Does your airline need to manipulate or correct data every time before the airline reliability report is published?
Does your airline have access to weather data?
Does your fleet use onboard data systems such as AHM or ACARS?
Is your airline's flight department willing to share Flight Data Recorder information with other departments?
Is your airline willing to share technical and operational data with other airlines that operate the same aircraft type(s)?
What now?
Have you answered the first four questions with yes?
Great you have entered the first stage of the process and are a reporting airline. This means you are able to produce analytical insight into your data, however it is mostly requiring some significant effort Typically your reliability engineer(s) spend a lot of time on producing reports and do not have sufficient time for further analysis. Also, as data is still dispersed thru various departments, time is spent on consolidation and alignment of data.
In case “no” was the answer to any of the first 3 questions, chances are high that your current data and system landscape is not complete enough to start implementing anything close to predictive maintenance or predictive analytics. Due to the lack of data or reduced data quality any outcome would be below required thresholds for adequate prediction models. The first next step to undertake is to perform a full holistic data scan on your MRO data to identify the required actions to proceed.
Have you answered all first nine questions with yes?
You have entered the stage of being a monitoring airline. Based on our experience we know that many airlines are in-between the first and second stages. Often EXCEL is still present in many airlines but there is some sort of platform or software in place that allows for direct analysis without data corrections. The highest risk in this stage is that it seems compelling to start looking at predictive analytics as lots of data is available, however, albeit available it might still not be enough for proper predictions.
In case “no” was the answer to either questions 4, 5, or 6, your first port of call will be your data quality. You have a suitable infrastructure in place, but not enough controls yet to ensure a proper level of data quality.
Have you answered all 13 questions with yes?
Congratulations, you are ready for the successful implementation of predictive models within your airline. You can start by using the Avilytics AOG Risk Monitor. Click here to request a demo now!
In case your answer to one of the last 4 questions is “no”, you are not there yet but don’t worry you are on the right track. Keep on working (and pushing) to achieve for example weather or FDR data for your predictive models to increase the reliability. Industry data might still be a far fetch but would enhance the models even more due to the benchmark.