Explaining Aviation Safety Incidents Using Deep Temporal
Multiple Instance Learning
Vijay Manikandan Janakiraman
USRA/ NASA Ames Research Center
Moffett Field, CA, USA
vjanakir@mail.nasa.gov

arXiv:1710.04749v2 [] 12 Feb 2018

ABSTRACT
Although aviation accidents are rare, safety incidents occur more
frequently and require a careful analysis to detect and mitigate
risks in a timely manner. Analyzing safety incidents using operational data and producing event-based explanations is invaluable to
airline companies as well as to governing organizations such as the
Federal Aviation Administration (FAA) in the United States. However, this task is challenging because of the complexity involved
in mining multi-dimensional heterogeneous time series data, the
lack of time-step-wise annotation of events in a flight, and the lack
of scalable tools to perform analysis over a large number of events.
In this work, we propose a precursor mining algorithm that identifies events in the multidimensional time series that are correlated
with the safety incident. Precursors are valuable to systems health
and safety monitoring and in explaining and forecasting safety
incidents. Current methods suffer from poor scalability to high dimensional time series data and are inefficient in capturing temporal
behavior. We propose an approach by combining multiple-instance
learning (MIL) and deep recurrent neural networks (DRNN) to take
advantage of MIL’s ability to learn using weakly supervised data
and DRNN’s ability to model temporal behavior. We describe the
algorithm, the data, the intuition behind taking a MIL approach,
and a comparative analysis of the proposed algorithm with baseline
models. We also discuss the application to a real-world aviation
safety problem using data from a commercial airline company and
discuss the model’s abilities and shortcomings, with some final
remarks about possible deployment directions.

KEYWORDS
Multiple Instance Learning, Deep Learning, Time Series, Aviation
Safety, Systems Health Management, Precursor

1

INTRODUCTION

Explanations for Aviation safety incidents may be required for a
multitude of reasons. A safety analyst working for a commercial
airlines company may be interested in finding the root causes of
incidents to improve the fleet’s safety, to inform predictive maintenance, to monitor human factors such as fatigue, and situational
awareness to improve pilot training etc. On the other hand, FAA
and NASA often require such explanations to inform better airspace
designs, improve policies, regulations and standard operating procedures. Organizations such as the National Transportation Safety
Board (NTSB) may benefit from having explanations to safety incidents while conducting accident investigations.

Currently, safety incidents are explained manually; a group of
human experts analyze a given safety incident and offer explanations using causal factors and correlated events that occurred in the
flight. However, this is not a scalable approach with the numerous
safety incidents that occur and with the growth in the volume of
sensory data. In the US National Airspace alone, more than 9 million
scheduled passenger flights operated in 2016 with an estimated 5000
overhead flights at any given time [2], generating large volumes
of data at different levels in the airspace system. A commercial
passenger airline with a fleet size of 280 operates about 1000 short
to medium range flights per day, flags about 900 safety incidents1 .
While these numbers are expected to grow, it becomes impossible
to manually analyze the numerous safety incidents on a case-bycase basis to come up with actionable insights to improve safety in
a timely manner. This motivates us to develop a system that can
analyze the flight data and offer explanations in an automated way.
We propose using precursors as a means to identify key events
in the flight time series data. A precursor is any correlated event
that occurs prior to the safety incident. Precursors give insights
into the root causes of the incident and provide actionable insights
with early alerts and corrective actions. To provide useful explanations, precursor mining aims to answer the following questions “When do degraded states begin to appear?" “What are the degraded
states?" “Are there corrective actions?" “What is the likelihood that
the safety incident will occur?". For example, our method identified
precursors to a particular high speed exceedance (safety incident)
and helped explain the incident as follows. During the landing phase
of the flight (see Figure 1), about 25 miles away from the runway,
most variables were normal indicating a “safe state." Correspondingly the probability of the safety incident was close to zero. At
about 2500ft altitude and 13 miles away, the flight made its turn to
align with the runway when the speed reference was set incorrectly
(unusually high) which caused the engine speed and consequentially the flight’s speed to be high. The increase in probability of
the safety incident indicates this as a precursor. At about 8 miles
out, the speed reference was corrected (reduced) which caused the
airspeed to drop. This is indicated by a decreasing probability of
failure indicating possible corrective actions. However, from about
1500 ft altitude, the likelihood increases because the flaps were delayed. Flaps are friction surfaces that help reduce airspeed. In this
case, flaps are not set on time which caused the airspeed to remain
high. This happened when the flight approached the 1000 ft altitude

1 The

incident statistics are based on exceedance counts obtained from a de-identified
airline company. The exceedances considered here fall under severity 2 and 3 while
severity 1 is often ignored because of its mildness. Also note not all exceedances have
equal safety concern.

•

~ 2500ft, 13 miles out
• Turn to final
Speed reference set incorrectly
• High engine speed

~ 1500ft, 5 miles out
• Flaps not set

Altitude

1000 ft, 3 miles out
Speed Exceedance
(adverse event)

~ 3500ft, 25 miles out
All variables normal
~ 2000ft, 8 miles out
Speed reference corrected

Probability of
adverse event

Flight timeline

Figure 1: Explanations in the form of key events identified by precursor mining algorithm for a flight that had a high speed
exceedance (safety incident). Important events are shown by blue arrows, precursors are marked with red circles while a green
star is used to indicate the safety incident.
marker that the safety incident was flagged. Thus, a 250 dimensional time series data was summarized to provide explanations
about key events that occurred prior to the safety incident.
Precursor mining is a weakly-supervised learning problem. Usually we have easy access to a label that indicates the occurrence of a
safety incident in a flight. The labels may come from aviation safety
reporting system (ASRS) [28] where the flight crew members and air
traffic controllers volunteer to report safety incidents during their
flight, or from exceedance reports that are automatically generated
by using domain based rules to identify safety incidents. While
a flight-level label is available, the labels for precursors within a
flight are usually not available, which makes the explanation task
challenging. To address this, our past work focused on using the
flight-level information as a weak supervision to obtain the expert’s
decision making behavior using inverse reinforcement learning [18].
In this paper, we propose to use Multiple-Instance Learning (MIL)
to use the flight-level label to infer time-step wise event labels.
A MIL setting typically assumes a set of data instances grouped
in the form of bags. The bag level labels are known while the instance labels are unknown. The task of MIL is to learn a model to
either predict the bag labels or instance labels or both. An assumption is usually made in MIL that relates the instance-level labels to
the bag-level labels. A standard assumption of MIL states that the
bag is labeled positive if there is at-least one positive instance in
the bag while it is labeled negative if all instances in the bag are
negative. A more detailed discussion on MIL and the assumptions
involved can be found in [5, 13, 14]. The standard MIL formulation
does not consider time connection between instances and its performance drops when applied to time series data [15]. To address this,
we propose a deep temporal multiple-instance learning (DT-MIL)

model that extends the MIL framework to be applied to time series
data. Further, by having deep neural network models, the approach
can be scaled better for large data sets using GPU parallelism. To
summarize, the main contributions of the paper include
(1) a novel deep temporal multiple-instance learning (DT-MIL)
framework that combines multiple-instance learning with
deep recurrent neural networks suitable for weakly-supervised
learning problems involving time series or sequential data.
(2) a novel approach to explaining safety incidents using precursors mined from data.
(3) detailed evaluation of the DT-MIL model using real-world
aviation data and comparison with baseline models.
(4) precursor analysis and explanation of a representative safety
incident using multi-dimensional flight data from a commercial airline.
The rest of the paper is organized as follows. We discuss related
work in Section 2. In Section 3, the precursor mining problem
is formulated with an intuition about our approach followed by
description of the DT-MIL model. The discussion about data, the
considered safety incident and experiments are in Section 4. In
Section 5, we present quantitative results of the DT-MIL method
with a comparative analysis using relevant baselines and discuss
the explanations offered for a few representative flights. This is
followed by conclusions in Section 6.

2

LITERATURE REVIEW

The application of data mining and machine learning methods for
aviation safety is not new. NASA Ames Research Center has been
pioneering this line of work for several years and has developed
2

methods such as Orca [8], i-Orca [9], Inductive Monitoring System [17], Multiple Kernel Anomaly Detection (MKAD) [11], ELM
based anomaly detection [20], among others. For event detection
and optimal alarm systems, NASA has open-sourced the Adverse
Condition and Critical Event Prediction Toolbox (ACCEPT) which
is a suite of machine learning algorithms [25]. Outside of NASA,
much of the work revolves around finding anomalies in flight data
[24, 29] or in text reports [4, 31]. Another related research is in
understanding human factors in flight safety [23]. The above work
aims at detecting unsafe patterns but offers little or no automated
explanations.
Recently, several approaches for precursor mining have been
proposed. The Automatic Discovery of Precursors in Time Series
(ADOPT) finds precursors using multidimensional sensory time
series data and has been applied to single flight incidents such as
a take-off stall risk [19], as well as to incidents such as go-around
[18] that involve interactions between multiple flights. Another
approach is using a nested multiple-instance learning that finds
precursors to protests and social events using text data [26]. Other
story telling based algorithms exist, for instance [16], that are aimed
at entity networks and using text documents but it is not trivial to
see its applicability for multidimensional sensory data. Compared
to the above methods, our approach combines multiple-instance
learning (MIL) and deep recurrent neural networks (DRNN) to take
advantage of MIL’s ability to model weakly-supervised data and
DRNN’s ability to model long term memory processes, to scale well
to high dimensional data and to large volumes of data using GPU
parallelism.
Multiple-instance learning was first proposed by Dietterich et al.
[13] which was then extended using support vector machines [5]
and neural networks [27]. There exists some work on using multiple
instance learning for temporal data such as using autoregressive
hidden Markov models [15], and using recurrent neural networks
[12] mainly to learn a prototype vector using the instances which is
then used in the recurrent network for supervised classification of
the bags. The motivation of this paper appears to be different from
ours and it does not take advantage of the superior capabilities of
long-term memory units such as the LSTM units. Only recently,
deep multiple-instance learning has been proposed and used in the
context of object detection and annotations [7, 30, 32]. Our work is
an extension to the prior literature by combining multiple-instance
learning with deep recurrent neural networks which appears to be
novel.

3

an instance in record i at time t be given by
(1)

P(safety incident|e ij ) > δ .

(2)

where f (.) is some function that captures a transformed representation of the sequence xi1 , xi2 , ..., xit . Given the data X i and the
corresponding safety incident label Yi , the goal of precursor mining
is to find a set of time instances Pi = {j}, 1 ≤ j ≤ L for which
Note that the labels are binary; i.e., Y i = 1 implies a safety incident
reported in X i . The label is also for the entire flight X i which is
a collection of instances e 1i , e 2i , .., eti , .., e Li . Each xit is a vector of d
sensory variables that may include airspeed, altitude, engine states,
pilot switch positions, autopilot modes among others.

3.2

Intuition

Following the definition from above, precursor mining is a weaklysupervised learning task. The only supervision comes from the
summary label for a flight that says if it had a safety incident or not.
Using this high-level information, the goal is to identify low-level
events that occurred during the flight that correlate to the safety
incident. Multiple-instance learning is a natural fit for weakly supervised problems where the bag labels (high-level supervision)
are used to infer the instance labels (low-level). Further, a flight
spans a finite time and events that happen during a flight are correlated temporally. For example, a flight typically involves dynamics
at multiple time scales. An increase in throttle causes a response
in engine speed in the order of milliseconds while it takes a few
seconds to cause a notable increase in the airspeed of the flight.
Also, when there is a change in wind condition or traffic pattern, it
takes much longer to see its effect reflected in the flight’s behavior.
Thus it is important to consider instances in the flight along with a
context history (seen in the definition of an instance eti in equation
(1)). Figure 2 shows our proposed idea in which each flight is a
multidimensional time series with a bag-level label that indicates
if the flight had a safety incident or not. Here the instances are
defined as the sequence of measurements up to the current time. In
this way, an instance at time t can be thought of as the event that
occurs at time t, given the context up to that time. The standard
MIL formulation does not capture temporal connection between
data instances [15], and we propose a MIL framework that address
this problem in the next section.

3.3

METHODOLOGY

The DT-MIL Model

In this section, we develop the deep temporal multiple-instance
learning model for precursor mining. Earlier, we defined an instance
eti by using a history of data measurements. To efficiently model
the instances, we use a specific type of recurrent neural networks
involving the gated recurrent unit (GRU) [10]. Motivated by long
short-term memory (LSTM) units, GRUs are proposed as a variant
to simplify computation and implementation. Compared to LSTM
which has a memory cell and four gating units that adaptively
control the information flow inside the unit, the GRU has only
two gating units [10]. Owing to its simplicity over LSTM units, we
choose GRU as the recurrent units for our model. The information
flow in a GRU is shown in Figure 3 while its governing equations

In this section, we add more formalism to the precursor mining
problem extending our earlier work [18], discuss the idea behind
using multiple-instance learning and introduce the deep temporal
multiple-instance learning (DT-MIL) framework.

3.1 Precursor Mining Problem
The problem of precursor mining can be stated as follows. Let
the time series data be represented by X with dimensions (N , L, d),
where N denotes the number of time series records, L the maximum
length of time series2 and d the number of sensory variables. Let
2 variable

eti = f (xi1 , xi2 , ..., xit ),

length sequences are accommodated.
3

Negative Bag
Nominal flight

Positive Bag
Safety incident
Figure 2: Figure showing the idea of a bag representation of a flight with instances corresponding to sequence data up to the
current time. A flight is labeled positive if a safety incident is reported.
layers may be added depending on the data complexity. This combination of recurrent and fully connected layers model the deep
representation function f (.) in the instance definition in equation
(1). The instances et are obtained after the fully connected layers
which are then fed into a logistic layer to convert the instances into
probabilities p1 , p2 , .., pt , .., p L . The MIL aggregation function a(.)
converts the instance probabilities into a bag probability ŷ as

1
-1

concat

sigmoid

tanh

ŷ i = a(p1i , p2i , .., pti , .., p Li ).

sigmoid
concat

The loss function for learning the model parameters is a binary
cross-entropy function given by
L(y, ŷ) = −[yloд(ŷ) + (1 − y)loд(1 − ŷ)].

Figure 3: Figure showing the data flow in a gated recurrent
unit (GRU).

=

rt

=

h̃t

=

ht

=

siдmoid(Wz .[ht −1 , x t ])

(3)

tanh(W .[r t ∗ ht −1 , x t ])

(5)

siдmoid(Wr .[ht −1 , x t ])

(4)

(1 − zt ) ∗ ht −1 + zt ∗ h̃t .

(6)

(8)

where y represents the bag label. The above setup can be easily
extended to a multi-class case by having many independent logistic
layers followed by MIL aggregation layers for each class. It must be
noted that multiple logistic layers may be better suited compared to
a soft-max layer because a flight may have multiple safety incidents
that may not be independent of each other. The model can then
be trained using standard backpropagation taking advantage of all
the recent advancements made in deep learning including GPU
parallelism.
The MIL aggregation is an important function that mimics the
MIL assumptions under the cross-entropy loss function. For example, if a(.) is set to a max(.), then it mimics the standard MIL
assumption. To see how this works, consider a negative bag with a
label of 0. To minimize the loss function, the ŷ will be pushed close
to 0. Given ŷ i = max(p1i , p2i , .., pti , .., p Li ) and when ŷ is made small,
the instance probabilities p1i , p2i , .., pti , .., p Li are all pushed close to
0 simultaneously. On the other hand, for a positive bag with a label
of 1, with the aggregation function being a max(.), at least one
of the instance probabilities will be pushed close to 1 so that ŷ
becomes 1. Other aggregation functions such as mean, weighted
mean, approximated max, Noisy-OR [33], Noisy-AND [22] may be
used to reflect different MIL behavior and assumptions.

are shown below
zt

(7)

The parameters of GRU include Wr ,Wz and W . The reset signal r t
determines if the previous hidden state should be ignored while
the update signal zt determines if the hidden state ht should be
updated with the new hidden state h˜t . By having many units each
having its own reset and update signals, the GRU learns to capture
the dependencies from past data over different time scales [10].
The DT-MIL architecture is shown in Figure 4. The time series
data is processed by GRU units which convert the sequence of data
into hidden states that are passed on to a layer of fully connected
tanh units. The recurrent layer captures the temporal dependencies
in the data while the fully connected layer adds more approximation
capability to the model. Additional recurrent and fully connected
4

flights that truly had a sustained speed exceedance and ignores
the ones that are momentarily caused by noisy data, winds etc. If
equation (10) is satisfied, the flight is labeled positive as having the
HSE safety incident. Note that the DT-MIL setup does not require
the time-step location of the safety incident. As long as the safety
incident is known to have occurred during the flight, it is labeled
positive4 .

Bag Label

MIL aggregation
Instance Probabilities

Logistic layer
LOG

et
FCL

LOG

et+1
FCL

LOG

et+2
FCL

LOG

Fully connected
layers

eL

4.3

FCL

RNN layers
GRU

GRU

GRU

Xt

Xt+1

Xt+2

GRU

Data

XL

Figure 4: Architecture of DT-MIL model showing the different layers.

4 EXPERIMENTS
4.1 Data
For this study, we use the Flight Operational Quality Assurance
(FOQA) data provided by a de-identified commercial airline3 operated between April 2010 to October 2011 in Europe. The FOQA data
consists of most of the sensory measurements on board the aircraft
including flight speed, altitude, flight control surfaces, thrust, engine power, fuel consumption, pilot switches, pitch, roll, pressure,
temperature among many others. The data is sampled at 1 Hz. Most
of the data used in this work are recorded from flights that takeoff
from these airports. To eliminate variability in aircraft characteristics, we consider only the Airbus models A319 and A320 because
they belong to the same weight category of interest in this work.

4.2

Safety Incident

Accident statistics [3] show that about half of the fatal accidents
occur during the last 4% of the flight - the final approach and landing.
One of the key causes of landing related accidents involve energy
mismanagement [1], which often lead to loss of control, tail-strike
before reaching the runway, runway overrun, hard landing etc. The
aircraft energy is a function of the weight, airspeed, altitude, thrust,
lift and drag forces, which requires careful monitoring and control
by the crew.
In this work, we consider a high-speed exceedance (HSE) during
landing as the safety incident. The HSE is a rule-based definition
for a safety incident that is widely used in operations to analyze
safety. HSE is defined as
airspeed[al t itude=1000f t ] > target + tolerance

(9)

airspeed[cont ex t ] > target[cont ex t ] + tolerance;

(10)

Model Setup

From the FOQA data, we selected a subset of sensory variables
for this study, excluding the ones that were clearly not useful for
explaining the HSE event. Some of the continuous variables that
were chosen include airspeed, altitude, angle of attack, speed target,
speed reference, aileron position, elevator position, rudder position,
stabilizer position, engine speed, pitch angle, roll angle, accelerations along the 3 axes, vertical speed, aircraft gross weight. Some
categorical variables include commands on flaps, speed brakes, landing gear, activations of autopilot, autothrottle and various modes
of the autopilot, flight director and flight such as flight director
engage status, location capture, altitude modes, thrust N1 mode,
thrust EPR mode, vertical speed mode, TCAS and STALL status
etc [19]. The FOQA data includes over 300 sensory variables out
of which we chose a subset of about 60 variables based on both
domain knowledge and automated feature selection using Granger
causality [19].
In about 15 months of operational data, we had about 500 flights
that had the HSE event. We considered the approach and landing
phases of the flight starting approximately 25 nautical miles away
from the runway. We sampled the flight sensory data at every quarter nautical mile of ground distance flown until touchdown. Thus,
each flight record is about 100 time steps long. Note that the DTMIL algorithm does not require the time series records to have the
same lengths. We split the data randomly into training, validation
and testing data in proportions 50%, 30% and 20% respectively of
the total data. The data was normalized to have a zero mean and
unit variance.
The DT-MIL model was prototyped using Tensorflow. The DTMIL model architecture is shown in Fig 4. It includes a GRU recurrent layer with 20 units, a fully connected tanh layer with 500
units, and a logistic layer to output the instance probabilities. A
max(.) MIL aggregation layer was used to mimic the standard MIL
assumption. The model was trained using one GPU on a Dell Precision workstation with 64GB of RAM. The ADAM [21] optimizer
with a learning rate of 0.002 was used. An L 2 regularization with
coefficient 0.01 was used for all model parameters. The best model
was identified by monitoring the performance on the validation
data.

where airspeed and target are recorded as part of the FOQA data. We
modify the HSE rule to include some temporal context as follows

5 RESULTS AND DISCUSSION
5.1 Quantitative Results
This section evaluates the performance of the proposed DT-MIL
model based on predictions at a bag (flight) level. As the speed

where the temporal context includes one nautical mile of flight
trajectory either side of the 1000 ft altitude checkpoint. By including
a temporal context around the check-point helps in identifying the

4 For

other safety incidents that may not be easily defined using simple thresholds
such as lack of crew awareness and air-traffic controller miscommunication issues,
the label may may be obtained from the Aviation Safety Reporting System reports or
from other relevant sources.

3 Owing

to proprietary nature of the work, we do not disclose the airline name and
data. We are making efforts to make the code available to the public.
5

exceedance is defined using airspeed, finding precursors in terms
of airspeed offer no novel insights and thus it is removed from the
analysis. We also eliminated variables that are highly correlated
with airspeed such as the ground speed, stabilizer position, above
stall margin etc to avid providing trivial information to the model
to classify the two classes of flights. For a comparative evaluation,
we include the following baseline models

models perform poorly on this data in spite of performing crossvalidation based hyper-parameter tuning. A possible reason could
be that the SVM models follow a heuristic approach to multiple
instance learning [6] and could have ended up in a local optima. Although time-embedding is done for MI-SVM (temporal), it doesn’t
capture the temporal behavior of the flight data sufficiently well
as compared to the DT-MIL model. The sophisticated GRU units
in DT-MIL model appears to capture the temporal behavior in the
data more effectively. ADOPT performs poorly in predicting the
bags which is expected. While it is not fair to compare ADOPT with
the other MIL algorithms, we still wanted to have it as a baseline
because ADOPT was our previous work on precursor discovery
and is relevant to the problem considered in this paper.

(1) MI-SVM: This is a support vector machine model that is
adapted for multiple-instance learning [6] following the standard MIL assumption that the bag is labeled positive if there
is at-least one positive instance in the bag while it is labeled
negative if all instances in the bag are negative. This is closest
to the DT-MIL with a max() aggregation function.
(2) MI-SVM (temporal): The above MI-SVM does not consider
the temporal connection between instances in a bag, which is
necessary for the flight time series data (refer Section 3.1). To
enable the MI-SVM model to capture temporal behavior, we
append each data instance to its past N H instances. The N H
is a hyper-parameter which is set based on cross-validation.
(3) DT-MIL (no temporal): This model is obtained by removing
the recurrent GRU units from the DT-MIL model. By doing so,
we remove the model’s ability to capture temporal behavior.
This is used as a baseline to quantify the benefits of the GRU
layer.
(4) DT-MIL (shallow): This model is obtained by removing the
fully connected layers from the DT-MIL model. By doing
so, we restrict the model’s learning ability and evaluate the
case where the model only has a recurrent layer without any
benefit from the dense layers.
(5) ADOPT: The automatic discovery of precursors in time series (ADOPT) algorithm from our previous work [18, 19].
ADOPT works based on using reinforcement learning techniques to model precursors. Although ADOPT does not fall
under the MIL setting and is not optimized for the bag-level
accuracy, it is still possible to obtain the bag-level AUC from
the instance level probability scores following the standard
MIL assumption.

Table 1: Area under the ROC curve (AUC) for bag-level predictions by DT-MIL compared against baseline models.

5.2

Models

Train AUC

Test AUC

DT-MIL
DT-MIL (no temporal)
DT-MIL (shallow)

0.9904
0.9723
0.9892

0.9837
0.9447
0.9789

MI-SVM
MI-SVM (temporal)

0.8052
0.8751

0.79825
0.8802

ADOPT

0.9373

0.8280

Instance Probabilities

From the trained DT-MIL model, we can obtain the instance level
probabilities by recording the inputs to the MIL aggregation layer
(see Figure 4). Using the instance probabilities, the significant events
that explain the HSE incident may be identified. Figure 5 shows the
instance probabilities for nominal flights and flights with the HSE
event for the unseen test data. It can be seen that most of the flights
(in both sub-figures) start the approach and landing phases from a
relatively “safe" state with adverse event probabilities close to zero,
which is expected. Instances that are about 25 nautical miles from
touchdown may be too far out to show significant precursors to the
HSE incident. As the flights approach the 1000 ft altitude instant
where the HSE event is defined, the instance probabilities increase
close to 1 (100% likelihood of HSE occurring) for the flights with
HSE event (in red), indicating that the instances have information
correlating to the HSE event. Such events may be considered as
precursors in the explanation task. On the other hand, for the
nominal flights (in green), the instance probabilities remain close to
zero most of the times, indicating absence of precursors in majority.
With the choice of a max(.) function for the MIL aggregation, if the
instance probability goes above 0.5 at any time during the flight, it
will be considered a positive flight. This happens for a few nominal
flights which accounts for the false positives. The instances that
have a high probability indicate possible precursors even in nominal
flights. However, for nominal flights, such precursors are usually
followed by corrective actions which decrease the probability close
to zero before the HSE checkpoint at 1000ft altitude (see discussion
that relate to Figure 9). In other cases, there are false positives too
(see discussion that relate to Figure 10). In the next section, we

For each model, the hyper-parameters are optimized using crossvalidation. The area under the receiver operating characteristic
(AUC) is used as the evaluation metric.
It can be seen from the results in Table 1, that the proposed DTMIL model was able to achieve a high accuracy in predicting the
adverse event. A breakdown of the benefits from the recurrent and
the dense layers can also be observed using the DT-MIL (no temporal) and DT-MIL (shallow) baseline models. It is not surprising to
see that the data is temporal and the adverse event is defined over a
time window which makes it difficult for the non-temporal models
(DT-MIL (no temporal) and MI-SVM) whose accuracies are the lowest compared to their temporal counterparts. It is also interesting
to note that DT-MIL (shallow) achieves similar accuracy as that of
the full DT-MIL model. One reason could be that the considered
adverse event is simple enough (function of two variables - airspeed
and speed target) to be captured by a shallow model. Note that for
a complex definition of an adverse event (defined using images or
text), the deep structure would make a larger impact. The SVM
6

Figure 6: Distribution showing early setting of final-flaps for
nominal flights (colored green) against the flights that had
the HSE event (colored in red).
precursor by DT-MIL. However, this flight is different from the previous one in that there are earlier precursors that may have caused
the airspeed to remain high even before the time of flap setting.
Indeed, a higher setting of speed reference and a corresponding
high engine speed were also detected as precursors by the DT-MIL
model when the flight was about 10 miles away and at an altitude
close to 3500 ft. This caused the airspeed to remain high along
with indications of the precursor score increasing. The precursor
variables are plotted along with altitude and airspeed indicating
that at this point, the speed reference was higher than nominal
which caused the engine speed to be high. This was then corrected
when the flight traveled about 3 miles closer to the runway, at which
point the autopilot was engaged. In spite of the correction in engine
speed, the airspeed continued to be high at the usual time of flap
setting. Thus it is possible that flaps were delayed because of the
high airspeed that was caused by the earlier precursors. Although
airspeed was removed from model training, the time instances of
high airspeed both at the safety incident as well as previous instances were correctly identified by the model. The precursor score
acts as a summary indicator of the risk factors that evolve in the
flight over time, indicates these time instances well.
While false positive rates are low (see Table 1, we analyze the
flights to understand model shortcomings. Figure 9 shows a nominal
flight (no speed exceedance) where a precursor was seen and later
corrected. At about 4000 ft altitude and 10 miles away from the
runway, the flight had some mild precursors. It appears that the
speed reference was set higher than nominal which caused the
engine speed to remain high, in turn causing the airspeed to remain
high momentarily. This was later corrected after the autopilot was
engaged which caused these variables to reduce to nominal values.
Although this flight is similar to the previous example in Figure 8,
here there is significant correction with no further precursors such
as late flaps which possibly prevented the safety incident. Although
this flight was flagged as a false positive, the instance probabilities
still offer some value in explaining the HSE related events that were
observed in the flight.
Finally, we analyze another false positive flight that is truly a
model error. Figure 10 shows a nominal flight whose variables were
nominal until about 2000ft altitude and 4 miles away from the

Figure 5: Instance probabilities of nominal flights (in green)
and flights with HSE safety incident (in red). Each subfigure
shows about 50 flights. The probabilities are plotted against
the flight timeline from 25 nautical miles from the runway
(left end of the plot) to the HSE event at 1000 ft altitude
checkpoint (right end of the plot).
explain the HSE events using the precursors and corrective actions
inferred from the instance probabilities.

5.3

Safety Incident Explanation

Using DT-MIL, we analyze two flights that had the HSE incident
and two nominal flights that were wrongly classified as a HSE flight
(false positives).
Figure 7 shows a flight that had the HSE incident. In this flight,
up to an altitude of 2000 ft (about 10 miles away from the runway),
most variables are within nominal bounds indicating a “safe" state.
When the flight reached about 1500ft altitude and about 5 miles
away from the runway, the usual trend is to set the flaps. The
flaps are friction surfaces that reduce the airspeed. However, this
was delayed which caused the airspeed to remain higher causing
the exceedance. In fact, based on the precursor insights, a further
analysis confirmed that the flap setting variable is highly indicative
of the high speed exceedance. Figure 6 shows the distributions
of the time instants when final-flaps are set for nominal flights
(colored green) against the flights that had the HSE event (colored
in red). About 85% of the nominal flights have the final flaps set
one nautical mile before the 1000 ft altitude checkpoint while only
12% of the HSE flights have the final flaps set at this time. Although
it is not clear if flap setting causally influence the airspeed but the
strong correlation emerged from the precursor analysis.
Figure 8 shows another HSE flight that was explained using
DT-MIL precursors. Until about 15 miles away from the runway,
the variables were nominal, again indicating a nominal or a “safe"
state. Towards the end of the flight, it can be seen that the flaps
were delayed similar to the previous example. This was detected as
7

1000 ft, 3 miles out
Speed Exceedance
(adverse event)

1000 ft, 3 miles out
No speed
exceedance

~ 4000ft, 20 miles out
All variables normal
~ 1500ft, 5 miles out
Airspeed higher
than normal

Abnormal
variables
corrected

~ 4000ft, 10 miles out
Turn to final
Speed reference high
Engine speed high

~ 1500ft, 5 miles out
Final flaps not set
Probability of
adverse event

Probability of
adverse event

Flight timeline

Flight timeline

Figure 7: Explanation for a flight that had a HSE incident
indicating a late flap setting precursor. Important events are
shown by blue arrows, precursors are marked in red while a
green star is used to indicate the HSE event.

Figure 9: Explanation for a false positive flight involving a
precursor followed by a correction.
1000 ft, 3 miles out
No speed
exceedance

1000 ft, 3 miles out
Speed Exceedance
(adverse event)

~ 3500ft, 10 miles out
Speed reference high
Engine speed high

~ 3500ft, 7 miles out
Autopilot engaged
Speed reference corrected
Engine speed normal

~ 2000ft, 4 miles out
Final flaps not set

~ 1500ft, 5 miles out
Final flaps not set

Probability of
adverse event
Probability of
adverse event

Flight timeline
Flight timeline

Figure 10: Explanation for a false positive flight where the
model wrongly identified late flaps as precursors.

Figure 8: Explanation for a flight that had a HSE incident
indicating a late flap setting precursor as well as an early
high engine speed precursor. Important events are shown
by blue arrows, precursors are marked in red while a green
star is used to indicate the HSE event.

6

CONCLUSION

This paper introduces a precursor mining framework for explaining
aviation safety incidents using flight recorded data. The DT-MIL
algorithm extends multiple instance learning to temporal data using
deep recurrent neural nets as building blocks. While the model is
general enough to be applied to find precursors to any event of interest (even outside of aviation safety), this paper demonstrates the
application using a high speed exceedance safety incident observed
in a real commercial airline data.
While this work provides early insights into finding precursors
and event explanations, further work must be done to fine-tune

runway. However, close to the checkpoint, the final flaps were not
set. Although this flight is similar to the first example (Figure 7),
there is no speed exceedance. In this flight, although the flaps were
delayed, the airspeed remains nominal in spite of the final flaps not
set. The DT-MIL model finds this as a high-risk precursor and gives
a high probability value although the HSE event is not flagged in
this flight.
8

the models to make it suitable for deployment. Some of the possible directions for deployment include (1) developing a decision
support system for the flight crew, air-traffic controllers and others
in the real-time decision making loop for flight operations and (2)
developing a data analytics pipeline to allow safety analysts and
airline personnel to make use of the DT-MIL model to conduct a
post-hoc analysis of flights to feed into pilot training or for designing operating procedures. For either of the tasks, our future work
will involve extending the DT-MIL framework to analyze multiple
safety incidents and formulate system-level safety margins to evaluate risks and risk mitigating maneuvers. Further, the models need
to be re-calibrated to find operationally significant precursors by
taking expert feedback.

7

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ACKNOWLEDGMENTS

This research is supported by the NASA Airspace Operation and
Safety Program. The authors would like to thank the subject matter
experts Michael Stewart, Bryan Matthews and Robert Lawrence
for their insightful comments and perspectives on the identified
precursors.

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