# Why do our aircraft take off slowly on the first of the month?

October 4, 2017

This was one of the more interesting discoveries of the last few weeks, and I thought that it would make a fun post for the flight data community. As a part of my role as a Data Scientist, it is one of my responsibilities to ensure that the data we record and monitor is of high enough quality to base business decisions on; that is, I have to make sure that our numbers make sense. As a part of an investigation into data quality problems, I was looking into a particular key point value1 called Groundspeed with Gear on Ground Max — which essentially is intended to record the maximum speed that an aircraft reaches when taking off or landing. Using this data, we can theoretically detect situations which may damage the landing gear and reduce fuel efficiency (and then alert the airlines accordingly).

To this end, I gathered a rather large dataset for this investigation (about 5 million flights from the last 18 months) and started to explore the distribution of this particular KPV. One of the first ports of call is to look at the average speed of aircraft over a given time period (in this case, per day). This gives me an of how things are changing (or even if they’re changing — they shouldn’t!). To my surprise, I did find some periodic fluctuations in this data; more specifically, the average takeoff speed across all aircraft would drop by about 20% on two days every month, and this seemed to be a regular occurrence. Let the head-scratching commence.

### A side note on data quality

Quality issues are very common with the sort of data that we deal with at Flight Data Services; not only are the analysts and data scientists quite far removed from the production of data (and thus certain trends can be difficult to interpret without a lot of domain knowledge), but the raw data also passes through several different transformations before any rigorous analysis is performed (i.e. conversion from flight data readout format to engineering units, signal processing and filtering, etc). This combination of factors (and a rather complex analysis toolchain) means that there are several different entry points for errors and it often requires a considerable amount of sleuthing to discover the underlying cause of the problem — this was no exception.

## The problem

As you can see in the plot below, the average speed for flights drops significantly on multiple occasions each month; quite frequently breaching the 99.5% prediction interval, which is obviously unexpected (and if real, quite concerning). For those unfamiliar with the idea, a prediction interval is simply a range that’s calculated such that 99.5% of a dataset is expected to lie within it; so, any data that falls outside of this range is quite unusual.

Drilling into this a little bit deeper, it does in fact appear that this average speed drop phenomenon happens on the same days each month; on the 1st and 11th of each month, to be precise. A clearer illustration of this is below (left), with vertical lines to clearly show the dates. After a little bit more investigation (and several very large cups of tea), I discovered that this drop in average speed is caused by huge surge in the number of what I call “zero-movement flights” - i.e. data in the flight recorder in which the aircraft doesn’t move. The plot thickens.

This situation was a prime candidate for applying some of the investigative techniques that I’d been employing in other projects, and I was able to apply a machine learning technique known as a “decision tree” (the details of this are quite technical, so I’ll leave it as an exercise for the reader to investigate further — but here is a good starting point). Essentially a decision tree is an algorithm that builds a tree-like diagram that iteratively splits a dataset into smaller bits until it has highlighted some of the attributes that are most overrepresented in a particular dataset (by maximising a metric called information gain). Decision trees are a very simple and very powerful machine learning technique; and they’re ideally suited for this sort of investigative modelling because it’s super simple to visualise them. Here’s one I made for an earlier post;

After building a couple of exploratory models and drawing up some visualisations from the results, it turned out that one of our customers’ airlines was performing scheduled aircraft maintenance on the 1st and 11th of each month. The aircraft would be switched on and tested or fixed (at which point the flight data recorders would kick in, despite the aircraft not going anywhere!). Further to that, the squat sensors in the landing gear would be flicked on and off hundreds (or thousands) of times — and due to the way that we process flight data, these sensor events would register as individual landings, even though the aircraft hadn’t moved! The data management staff at the airline are supposed to record these flights using a different kind of flight record, but obviously someone hadn’t got the memo! This had happened every month regularly for approximately 18 months, meaning that there were hundreds of thousands of these “zero-movement” flights just sitting there in the database, skewing the average speeds that I was trying to investigate. Once these flights were excluded from the dataset, the data looked much more sensible (hooray!).

Mystery solved.

1. We call these “KPVs” for short — essentially sensor values recorded at a specific point in a flight