# SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset
for Predictive Maintenance

Zahra Kharazian  Stockholm University, Department of Computer and Systems
Sciences, Kista, SE-164 07, Sweden  Tony Lindgren  Stockholm University,
Department of Computer and Systems Sciences, Kista, SE-164 07, Sweden  Scania
CV, Strategic Product Planning and Advanced Analytics, Södertälje, SE-151 32,
Sweden  Sindri Magnússon  Stockholm University, Department of Computer and
Systems Sciences, Kista, SE-164 07, Sweden  Olof Steinert  Scania CV,
Strategic Product Planning and Advanced Analytics, Södertälje, SE-151 32,
Sweden  Oskar Andersson Reyna  Scania CV, Connected Intelligence, Södertälje,
SE-151 32, Sweden

###### Abstract

This paper presents a description of a real-world, multivariate time series
dataset collected from an anonymized engine component (called Component X) of
a fleet of trucks from SCANIA, Sweden. This dataset includes diverse variables
capturing detailed operational data, repair records, and specifications of
trucks while maintaining confidentiality by anonymization. It is well-suited
for a range of machine learning applications, such as classification,
regression, survival analysis, and anomaly detection, particularly when
applied to predictive maintenance scenarios. The large population size and
variety of features in the format of histograms and numerical counters, along
with the inclusion of temporal information, make this real-world dataset
unique in the field. The objective of releasing this dataset is to give a
broad range of researchers the possibility of working with real-world data
from an internationally well-known company and introduce a standard benchmark
to the predictive maintenance field, fostering reproducible research.

##  1 Background & Summary

In an era marked by technological advancement and data-driven decision-making,
the automotive industry is undergoing a transformative shift, particularly in
the realm of vehicle maintenance. Predictive maintenance (PdM), has emerged as
a key player in revolutionizing how we care for and optimize the performance
of vehicles. This innovative methodology harnesses the power of advanced
analytics, sensor technology, and machine learning to predict when vehicle
components are likely to fail, allowing for timely and cost-effective
maintenance interventions. PdM is paramount for critical components in trucks.
This proactive approach to maintenance involves using data, analytics, and
monitoring systems to estimate the components’ health state.

One of the significant challenges in PdM is the shortage of public real-world
datasets. The reason is that Original Equipment Manufacturers (OEMs) tend to
keep the data to themselves and do not share it with anyone outside their
company. Exceptions are research or development partners who can access the
data when working with companies for PdM solutions. Reasons for keeping the
data hidden are that it is sensitive as it contains failure frequencies, what
type of sensor is available, etc. However, real-world datasets are crucial for
developing the methodology to create robust models for foreseeing real
industry equipment failures. The lack of data makes researchers in the field
use simulated datasets that mimic real conditions like C-MAPSS from NASA [1].
Synthetic datasets usually lack the complexity of real-world data and overlook
many common challenges, such as imbalanced labels, censored readouts, noise,
and intricate relations between signals.

The newly released real-world dataset from SCANIA is an exception to the
typical practice of companies not releasing data publicly and holds
significant potential for advancing the field of PdM. Another advantage
inherent in this dataset that makes it unique is its consideration of temporal
information, demonstrating gradual degradation in equipment in the form of
time series readouts. This multi-variate time series dataset is likely rich in
diverse information collected from SCANIA trucks and can be used for various
tasks in PdM, such as classification, regression, forecasting, anomaly
detection, and survival analysis. The proposed dataset is introduced for the
ongoing Industrial Challenge 2024 at the $22^{nd}$ International Symposium on
Intelligent Data Analysis (IDA) (link) with the title of Developing an
Effective Predictive Model for Imminent Component X Failures in Heavy-Duty
SCANIA Trucks at Stockholm University, Sweden. The setting of this industrial
challenge served as a nexus for collaboration, fostering a unique synergy
between academia and industry. The proposed dataset opens the door to various
opportunities for advancing research and optimizing PdM methodologies. The
dataset is publically published and is accessible on (link)[2]. This dataset
includes operational data, truck specifications, and the repair records of an
anonymized truck engine component called component X. The name of the
component has been anonymized for proprietary reasons. The wealth of
information in the dataset empowers decision-makers to make informed choices
regarding maintenance strategies, resource allocation, and fleet management.
This dataset has been used in one of our research on PdM previously, like [3].

Another comparable dataset also comes from SCANIA, it was concerned with Air
Pressure System (APS) [4] of trucks and was disclosed in a preceding
industrial challenge 2016 at the 15th International Symposium on Intelligent
Data Analysis (IDA), Stockholm University. The APS dataset is also suitable
for many machine learning tasks in PdM field and has been used in various
researches, such as [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. Although
the APS dataset is rich in the field, it does not capture the temporal
information of variables.

##  2 Methods

###  2.1 Data collection

The proposed dataset includes three sources of information encompassing
important aspects of truck information. The first part comprises operational
data. The second source contains repair records obtained from workshops. The
last one incorporates the specifications of trucks. Collecting such
informations from a fleet of trucks operating every day in different
situations involves several challenges and potential errors. Below, we explain
how the data is collected and also mention some of the possible errors during
data collection. Finally, we explain what considerations have been made to
protect the confidentiality of data for publishing.

####  2.1.1 Operational data

For collecting operational data of this dataset, trucks’ onboard sensors are
utilized to monitor and collect crucial parameters like real-time data on the
truck’s condition and performance. This data is stored inside the vehicle
control units and is accessible remotely or using plugin cables in visiting
workshops. One challenge in compiling operational data is losing connection
with devices that collect data. One example of this error happens with
Electronic Control Units (ECUs), like the engine control unit; sometimes, when
the ECU software is updated, the data collection counters could be reset,
i.e., start again from the beginning. This type of problem with data is
handled through post-processing of the data when it has been downloaded from
the truck. The post-processing does cover the majority of the possible types
of corrupt data when collecting information but it might not cover all cases.

####  2.1.2 Repair records

Repair records collected from trucks include information about maintenance,
repairs, and servicing performed on the vehicles. This information is usually
collected from invoices and work orders, including crucial information about
services rendered, replaced parts, labor costs, and other expenses incurred
during the workshop visits. If the components have been changed or marked as
repaired, they will be considered as failed and labeled as such in the
dataset. Otherwise, it will be considered as healthy. Here, the challenge is
that SCANIA can receive information only from its own network and official
workshops. Therefore, we limited the population of the vehicles to those with
a complete service history.

####  2.1.3 Specifications of trucks

Specification data is collected with the production system. Where they provide
detailed specifications for each truck’s model the company has produced, this
includes information about the engine type, weight capacities, dimensions, and
other technical details. One possible challenge in collecting such data is, in
very rare cases, when the truck is rebuilt and no longer matches the original
specification.

Some of the mentioned errors are handled or addressed using quality control
measures and regular equipment maintenance. Also, collaboration between data
scientists, engineers, and domain experts has been done as a crucial step for
developing effective strategies to handle data collection challenges in PdM.

###  2.2 Preserving Data Privacy

After collecting data, some modifications have been made to protect the
confidentiality of data for publishing. Regarding temporal representation
description, relative times are reported instead of the original timestamps.
This is done to capture temporal patterns without revealing specific dates or
time points. Repair frequencies and readout frequencies may have been modified
and are not necessarily representative of actual truck usage. Moreover,
variable names have been omitted for privacy and proprietary reasons,
presenting a subset of all available operational and specification data. Also,
the dataset comprises a random subset of vehicles visiting SCANIA workshops,
and real vehicle identity numbers are not disclosed and are reported as
anonymous IDs. The chosen subset is representative of SCANIA workshop visits
for Component X analysis. In addition to these considerations, some
perturbations like scaling have been made on operational data and repair
rates. It is worth noting that the perturbed data still maintains its utility
for predictive modeling. To evaluate the robustness of the model, a
sensitivity analysis is conducted, assessing how changes in perturbation
levels impact the model’s performance. This allowed us to observe model
effectiveness variations under different perturbation conditions. Furthermore,
it is validated that perturbations do not compromise the predictive model’s
accuracy.

##  3 Data Records

The proposed dataset is available on the webpage for the industrial challenge
of Symposium on Intelligent Data Analysis (IDA 2024) with the link. The
dataset is split into three parts: train, validation, and test. Each of these
parts comprises multiple files, with detailed descriptions provided for each
file in this section.

###  3.1 Train set

The training set contains three files including
"train_operational_readouts.csv", "train_tte.csv", and
"train_specifications.csv," where each is explained below.

####  3.1.1 train_operational_readouts.csv

Operational data from Component X in SCANIA trucks is collected in a file
named "train_operational_readouts.csv". This data file comprises of readouts
collected at different times from a variety of features for a fleet of
vehicles, yielding a multi-variate time series. These readouts, found in each
row, offer unique insights into the operational status of these vehicles. Each
readout encapsulates a distinct set of variables gathered between two points
in time, such as $t_{\\_}{i}$ and $t_{\\_}{i+1}$. In summary, it consists of
1122452 observations or instances from 23550 unique vehicles and 107 columns,
including vehicle_id and time_step. The column time_step acts as a gauge,
measuring the duration in time_step that each vehicle has been utilizing
Component X during its operational lifespan. Note that vehicles do not
necessarily follow the same sampling frequency in the time_step column. It is
also worth noting that this dataset does not encompass the entire available
operational data but rather represents a carefully selected subset. Experts
have specifically curated this subset, handpicking data they believe is most
relevant.

Ultimately, 14 attributes are selected and anonymized in the operational data,
offering a broad spectrum of information without divulging specifics about the
nature of Component X. These variables are organized into single numerical
counters and histograms with several bins to make this data more manageable
and insightful. In a histogram structure, each bin represents certain
conditions linked to the values observed within the measured features. For
instance, imagine a histogram linked to the variable distance_driven with four
bins representing ambient temperature. This histogram shows the distribution
of distance driven, organized into bins with different temperature ranges
$\\{(T<-20),(-20\leq T<0),(0\leq T<20),(T>20)\\}$. Each bar of this histogram
shows a temperature range, and the height of each bar represents the frequency
of distance_driven within that temperature range. Histogram variables use the
following indexing format: variableid_binindex. Where the "variableid"
represents the ID of an anonymized variable or feature, and "binindex" shows
the bin numbers. As an example, the variable with "variableid" 167 is a multi-
dimensional histogram that has ten bins, "167_0", "167_1",…, and "167_9".

In summary, six out of 14 variables are organized into six histograms with
variable IDs: "167", "272", "291", "158", "459", and "397," with 10, 10, 11,
10, 20, and 36 bins, respectively. Figure 1 illustrates two histogram features
of variables 167 (see Fig. 1(a)) and 459 (see Fig. 1(b)) from an arbitrary
vehicle at its last readout. Moreover, the eight rest of the variables named
"171_0", "666_0", "427_0", "837_0", "309_0", "835_0", "370_0", "100_0"] are
numerical counters. These features are accumulative and are suitable for the
representation of trends over time. Figure 2 visualizes these numerical
counters in more detail. An example of these features for an arbitrary vehicle
in this dataset is depicted in Fig. 2(a).

(a) Variable 167

(b) Variable 459

Figure 1: Visualization of two histogram variables for a random vehicle

(a) Non-histogram features over time

(b) Correlation of non-histogram features in train set

Figure 2: Non histogram variables for a random vehicle

Additionally, the correlation between these non-histogram features (numerical
counters) is calculated considering all readouts of vehicles and illustrated
in Fig. 2(b). As can be seen, all the features are positively correlated, and
no negative correlation exists between them. i.e., if the value of one feature
increases, the value of the other feature also tends to increase.

The distribution of missing values in train_operational_readouts.csv is shown
in Fig. 3. This data file includes a minimal amount of missing values with
less than 1 percent missingness per feature/column. Given the low percentage
of missing values, the dataset’s integrity remains relatively intact, allowing
various machine learning tasks with minimal preprocessing.

Figure 3: The missing value percentage in train_operational_readouts.csv file
is less than 1% per feature column. Note that the y-axis shows the percentage
of missing and is limited to 5 for the sake of better visualization.

####  3.1.2 train_tte.csv

The file with the name "train_tte.csv" contains the repair records of
Component X collected from each vehicle, indicating the time_to_event (tte),
i.e., the replacement time for Component X during the study period. This data
file includes 23550 number of rows and two columns:
"length_of_study_time_step" and "in_study_repair," where the former indicates
the number of operation time steps after Component X started working. The
latter is the class label, where it’s set to 1 if Component X was repaired at
the time equal to its corresponding length_of_study_time_step, or it can take
the value of zero in case no failure or repair event occurs during the first
length_of_study_time_step of operation. It is good to mention that the
"train_tte.csv" data is imbalanced with 21278 occurrences of label 0 and 2272
instances of label 1. In other words, it is skewed toward label 0. Figure 4(a)
compares the number of healthy and repaired components in the train set in
general. Moreover, Fig. 4(b) shows the distribution of healthy and repaired
components in their corresponding observation time during the data collection,
i.e., the time between the last and first readout for each vehicle.
Furthermore, there are no missing values (shown by NaN) in this data file.

(a) Repair distribution

(b) Distribution of healthy and failed components with different observation
times during data collection

Figure 4: Visualization of classes in the train set

####  3.1.3 train_specifications.csv

The last file in the training set is called "train_specifications.csv," which
contains information about the specifications of the vehicles, such as their
engine type and wheel configuration. In total, there are 23550 observations
and eight categorical features for all vehicles. The features in
train_specifications.csv are anonymized, each can take categories in Cat0,
Cat1, …, Cat8. Figure 5 illustrates the categories of each feature in the
train_specifications.csv file. Moreover, this data file has no missing value.

Figure 5: Frequency analysis of specification for training set

###  3.2 Validation set

The validation set consists of three files called "validation_labels.csv",
"validation_operational_readouts.csv", and "validation_specification.csv".

####  3.2.1 validation_operational_readouts.csv

In general, the validation_operational_readouts.csv has the same description
as the train_operational_readouts.csv except for the fact that in
validation_operational_readouts.csv, the operational data is incomplete. Only
a subset of the whole observations of each vehicle is provided where it
extends only up to a randomly selected readout. As a result, it lacks details
about the entire lifespan of a vehicle. This is done to simulate the usage of
a prediction model in a realistic scenario when it only has information about
a vehicle up until the present time. Figure 6 illustrates an example of a
hypothetical health indicator or degradation model of component X installed on
a vehicle in the validation set. Green dots are recorded readouts during the
time from the start of its operation, and the yellow star represents the last
simulated readout for this vehicle that is randomly selected among all
possible readouts. This means that we only have information up to that
readout, and the rest of the information is not given.

Figure 6: Class interpretation in validation and test data

Overall, it includes 196227 observations/rows showing the number of instances
from 5046 vehicles and includes 107 columns. Moreover, similar to
train_operational_readouts.csv, it contains a minimal missing value (less than
one percent for each feature).

####  3.2.2 validation_labels.csv

The validation_labels.csv file has 5046 rows, which is equal to the number of
vehicles contributed to the operational data of the validation set. It
includes a column named class_label, corresponding to the class for the last
readout of each vehicle.

As mentioned in subsection validation_operational_readouts.csv, the last
readout for the validation set is selected randomly among all readouts for
each vehicle. The temporal placement of this final simulated readout is
categorized into five classes denoted by 0, 1, 2, 3, 4 where they are related
to readouts within a time window of: (more than 48), (48 to 24), (24 to 12),
(12 to 6), and (6 to 0) time_step before the failure, respectively. These
classes show the time windows in which the last readouts for each vehicle are
randomly selected. For instance, in Fig. 6, the last simulated readout is
given in the time window of class 2.

This data set is also imbalanced and is skewed toward class 0. Table 1 shows
number of vehicles with the last readout assigned to each of these five
classes.

Table 1: The number of samples with the last readout assigned to different simulated classes. Label | Readouts
---|---
0 | 4910
1 | 16
2 | 14
3 | 30
4 | 76

For better visualization of the validation data, two techniques for
dimensionality reduction, called Principal Component Analysis (PCA) and
t-distributed Stochastic Neighbor Embedding (t-SNE), are performed on the last
readout from each vehicle in the validation set. Figure 7(a) and Fig. 7(b)
show the dataset when its dimension is reduced into two, using PCA and t-SNE
techniques, respectively. Vehicles belonging to each class are shown in
different colors. As can be seen, the five classes of the dataset are
scrambled in the two-dimensional space both for PCA and t-SNE. This
demonstrates the complexity of the problem when the features are projected
into two dimensions.

(a) PCA analysis of the validation set

(b) t-SNE analysis of validation set

Figure 7: Visualization of validation data using dimensionality reduction
techniques. Classes are shown in different colors.

####  3.2.3 validation_specification.csv

The file validation_specification.csv has a similar structure as
train_specifications.csv except the data is collected from 5046 vehicles in
the validation set. Details of categorical values of each feature are shown in
Fig. 8. Additionally, it has no missing values.

Figure 8: Frequency analysis of specification for validation set

###  3.3 Test set

The test set contains two files "test_operational_readouts.csv", and
"test_specifications.csv." Compared to the training and validation set, this
part of the data does not include information about the failure time or label
for Component X since the goal is to predict the label or the component’s
lifetime in the industrial challenge of the IDA 2024 conference. Once the 2024
IDA Industrial Challenge is over the true class labels of the test set will be
revealed.

####  3.3.1 test_operational_readouts.csv

Similar to validation_operational_readouts.csv, for the
test_operational_readouts.csv, the last readouts of vehicles are randomly
selected from the larger sequences observed during the study period (see Fig.
6) to emulates real-life situations. In summary, this file contains 198140
number of readouts from 14 variables (107 columns) gathered from 5045 unique
vehicles. Similar to validation_operational_readouts.csv and
train_operational_readouts.csv, the percentage of missing values in this file
is less than one percent for each feature.

####  3.3.2 test_specifications.csv

Specification information of 5045 test vehicles is collected in
test_specifications.csv file containing categorical values varying between
Cat0, Cat1,…, and Cat8. Furthermore, this file has no missing values.

##  4 Technical Validation

OEMs usually hesitate to share their device operational data for many reasons,
such as GDPR compliance, confidentiality agreements (NDAs), ownership of data,
device failure rates, etc. As a result, many works on data-driven PdM are
limited to private datasets [18, 19, 20, 21, 22, 23]. This prevents
researchers who worked with real data comparing their methods performances
with each other. The newly shared dataset by SCANIA can serve as a benchmark
in the field, making it easier for future researchers to compare their methods
and generate reproducible results.

The proposed dataset is highly conducive to various machine learning tasks
like regression, anomaly detection, survival analysis, and classification, in
the PdM field. This merit arises from two supreme characteristics: being a
real-world dataset collected from actual trucks and exhibiting a multi-variate
time series structure. In regression tasks, this dataset is valuable for
estimating components’ remaining useful life or predicting the time until the
next repair. Leveraging historical and time-to-event data, regression models
can provide insightful estimates. Survival analysis is a powerful technique
that predicts the survival function and the probability of an event happening
at a specific time while considering censored data in the training phase. The
blue vehicle populations in Fig 4 are referred to as censored data. The
temporal structure of the dataset is crucial for the effective application of
survival analysis. Furthermore, for classification tasks, using time series
data, the model can classify whether a vehicle is going to fail within a
specific time window or not.

To evaluate the performance of the aforementioned models, a cost function is
suggested by experts of the company, which is defined by the sum of the
different “Cost_n_m” multiplied by the number of instances, resulting in a
summarized cost (Total_cost).

$Total\\_cost=Cost\\_n\\_m\times No\\_instances$ (1)

Equation 1 calculates the total cost function, where n shows the actual class,
and m shows the predicted class, while n, m $\in$ {0,1,2,3,4}. In general,
when n<m, the Cost_n_m indicates a cost for a false positive error, and if
n>m, it indicates a cost for a false negative error. It should be emphasized
that the cost of false negative prediction is much higher than that of false
positive prediction. Table 2 demonstrates the different values of Cost_n_m
according to different values of actual and predicted class prediction. In
this table, the term "Cost_0_{1,2,3,4}" denotes the cost incurred when a
mechanic conducts an unnecessary check at a workshop. On the other hand,
"Cost_{1,2,3,4}_m" represents the cost associated with the possibility of
missing a faulty truck or triggering an alarm too late. This delay could lead
to a breakdown or necessitate costly adjustments to the transportation plan
for the customer.

Table 2: Table of prediction cost | Predicted: 0 | Predicted: 1 | Predicted: 2 | Predicted: 3 | Predicted: 4
---|---|---|---|---|---
Actual: 0 |  | Cost_0_1=7 | Cost_0_2=8 | Cost_0_3=9 | Cost_0_4=10
Actual: 1 | Cost_1_0=200 |  | Cost_1_2=7 | Cost_1_3=8 | Cost_1_4=9
Actual: 2 | Cost_2_0=300 | Cost_2_1=200 |  | Cost_2_3=7 | Cost_2_4=8
Actual: 3 | Cost_3_0=400 | Cost_3_1=300 | Cost_3_2=200 |  | Cost_3_4=7
Actual: 4 | Cost_4_0=500 | Cost_4_1=400 | Cost_4_2=300 | Cost_4_3=200 |

##  5 Data availability

The proposed dataset collected from component X of SCANIA trucks is publicly
available and licensed under CC BY 4.0[24]. This dataset consists of seven
individual data files, all in CSV (Comma-Separated Values) format. Please note
that the data file containing the labels for the test set is currently
unavailable since this dataset was released for the ongoing IDA 2024
industrial challenge. The goal is to estimate these labels for the test set,
and the corresponding data file will be published when the industrial
challenge is concluded.

##  6 Acknowledgements

This work has been funded by Scania CV AB and the Vinnova Program for
Strategic Vehicle Research and Innovation (FFI).

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