Acta Astronautica 238 (2026) 739–745
Contents lists available at ScienceDirect
Acta Astronautica
journal homepage: www.elsevier.com/locate/actaastro
Research paper
Transformer-based anomaly detection for satellite telemetry data
Asma Fejjari a ,∗, Alexis Delavault b , Robert Camilleri b , Gianluca Valentino a
a
b
Department of Communications and Computer Engineering, University of Malta, 2080, Msida, Malta
Institute of Aerospace Technologies, University of Malta, 2080, Msida, Malta
ARTICLE
INFO
Keywords:
Time series
Anomaly detection
Satellite telemetry data
Transformers
ESA OPS-SAT
ABSTRACT
Time series anomaly detection can help identify serious issues in complex systems, and can potentially
reduce the risk of failures or operational disruptions by providing advance warning. Over the past decades,
several methods, ranging from out-of-limit techniques to machine learning models have been developed to
automate anomaly detection for satellite telemetry data. In recent years, transformer-based architectures have
demonstrated considerable success in the problem of time series anomaly detection. In this paper, we present
and compare the performance of various transformer architectures in detecting anomalies in satellite telemetry
data, including the recently published ESA OPS-SAT telemetry dataset, and show how these architectures
outperform the benchmarks conducted on this dataset.
1. Introduction
As the complexity and number of satellite constellations continues
to increase, the likelihood of anomalies occurring in telemetry data is
also rising [1,2]. Anomalous behavior in such systems poses potential
risks that can threaten the safety of satellite missions. The swift identification of anomalies in satellite telemetry time series data plays a
significant role in enhancing safety and minimizing such risks [3,4].
In recent years, numerous anomaly detection (AD) methods have
been explored and exploited to identify anomalous patterns in telemetry data. Out-of-limit (OOL) systems [5,6] are the classical and most
commonly-used approaches in this field. They are based on predefined
nominal limits that allows to determine what is considered an anomalous within the data. Due to the increasingly complex interactions
between the telemetry parameters, the growing volume of data as well
as need for time-consuming analyses, the use of conventional OOL
algorithms is becoming more difficult and expensive.
The use of machine learning (ML) models in AD dates back many
years [7]. Due to their ability to handle highly-dimensional data,
ML methods such as Support Vector Machine (SVM) [8,9], k-nearest
neighbors [10,11] and Principal Component Analysis(PCA) [12] have
been shown to be effective in various time series AD tasks. However,
these approaches present some shortcomings when the telemetry data
include complex or high volume telemetry data.
Another major trend in satellite AD is the use of deep learning
(DL) models [13,14]. DL approaches have shown enormous capabilities in learning accurate representations of complex telemetry data,
compared to the conventional OOL and ML methods. Due to their
ability to perform long-range relational modeling and processing of
spatial and temporal features in the data, DL models such as convolutional neural networks (CNNs) [15,16] and recurrent neural networks
(RNNs) [17,18] have become a popular choice to detect anomalies with
high performance. However, it is still difficult for CNNs and RNNs
to model long-range temporal dependencies effectively. Furthermore,
these models are considered slow and computationally expensive.
Currently, the state-of-art of unsupervised AD in time series consists of Transformers [19–25]. A transformer is a deep learning architecture capable of learning and modeling temporal dependencies
within a sequence input using the attention mechanism. Using the selfattention mechanism, Transformers have shown superior performance
in capturing long-term dependencies and learning dynamic patterns of
sequential data, offering benefits over traditional CNNs or RNNs models
and bring high detection quality for modeling satellite telemetry data.
The evaluation of Anomaly Transformer detection applications is based
on the availability of public datasets for training. Given the difficulty
and high cost of obtaining annotated satellite telemetry data, there is a
limited number of datasets available for AD. In fact, most papers in the
literature use the NASA Soil Moisture Active Passive (SMAP) and Mars
Science Laboratory (MSL) datasets.
The objective of this paper is to identify anomalies in a new benchmark dataset called ESA OPS-SAT using Transformer models. This satellite telemetry data derives from the OPS-SAT CubeSat mission, which
was operated by ESA. In summary, this paper makes the following three
main contributions:
∗ Corresponding author.
E-mail address: afejj01@um.edu.mt (A. Fejjari).
https://doi.org/10.1016/j.actaastro.2025.09.035
Received 22 April 2025; Received in revised form 10 July 2025; Accepted 10 September 2025
Available online 23 September 2025
0094-5765/© 2025 The Authors.
Published by Elsevier Ltd on behalf of IAA. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Acta Astronautica 238 (2026) 739–745
A. Fejjari et al.
• Review a number of relevant Transformer approaches in satellite
AD, summarizing the state-of-the-art.
• Conduct extensive experiments that examine and compare the
state-of-the-art transformer AD methods on the OPS-SAT dataset.
• Highlight future research directions in time series Transformer
applications.
series data using a GAN-style adversarial training approach. TranAD architecture leverages a two-phase adversarial training process to extract
both temporal context information and small deviation of anomaly,
i.e., anomalies relatively close to normal data, by amplifying the reconstruction errors. Capturing short-term temporal trends helps to prevent
false positives in the input sequences. Contrary to the Vanilla Transformer, the adversarial training procedure employs two Transformer
encoders and two Transformer decoders to gain stability and speed
up training of the model. Besides introducing model-agnostic meta
learning (MAML) to train the model using limited data, TranAD also
uses multi-head self attention operations to speed up training and
testing processes.
The different contributions introduced by the TranAD model allow
to achieve high detection performance for six different time series
datasets. For instance, the experimental study have shown that the
TranAD anomaly detector is able to achieve a precision of 85% and a
recall of 99.99% for the NASA SMAP-MSL dataset with a significantly
reduced training time.
The remainder of the paper is structured as follows. In Section 2,
we introduce the key components of Transformer architecture. Then,
we review the related Transformer variants in time series AD. Section 3 presents the details of the experimental study and the results
obtained. Finally, conclusions are drawn and future research pathways
are considered in Section 4.
2. Transformers in satellite anomaly detection
Transformers are DL models that aim to capture complex patterns
and dependencies in sequences using the self-attention mechanism.
These models were originally crafted for machine translation [26].
Later works have shown that Transformer models can be adapted to
handle sequential data in other fields such as speech recognition [27],
natural language processing [28] and time series analysis [19].
Recently, Transformers have seen an important advancement in the
field of satellite data analysis. In fact, the satellite system generates
significant time series telemetry data volumes with high dimension
and complex relationships between telemetry parameters. Unlike RNNs
models, which can struggle with long sequences due to vanishing gradients, Transformers are able to handle long sequences, making them
an appropriate choice for complex time series patterns including the
satellite telemetry data. In addition, Transformers are able to analyze
all time steps simultaneously and understand the relationships between
distant points in the sequence, due to the self-attention mechanism.
Time series telemetry data require this crucial concept to account for
past events that can have a significant impact on future values [29,30].
Rapid detection and subsequent notification of any anomalous occurrences related to the functionality of satellite subsystems are very
important to guarantee their safe operation. Transformers can process
all positions in a sequence concurrently, making them faster and more
efficient than RNNs that process data sequentially [31].
Due to this favorable result, a variety of Transformer variants have
been developed in recent years. These variants have upgraded the
vanilla Transformer in different aspects.
2.3. Anomaly transformer: Time series anomaly detection with association
discrepancy
The Anomaly Transformer [23] is an unsupervised anomaly detection approach which explores the association learning to highlight the
distinguish-ability between normal and abnormal data points. Unlike
the previous Transformer models, Anomaly Transformer uses association discrepancy observation to replace the self-attention mechanism
by the Anomaly-Attention one. The latter allows for finding more
informative distinguishable associations by learning the association
discrepancy.
The Anomaly-Attention incorporates a dual-branch structure to
model prior- and series- associations of each time point respectively.
The prior-association employs the learnable Gaussian kernel to compute the prior regarding the relative temporal distance of each time
point. While series-association branch corresponds to the self-attention
weights extracted from raw series. In addition to reconstruction loss, a
minimax strategy is applied between the two branches to amplify the
normal-abnormal distinguishability and optimize the model.
Anomaly Transformer approach has obtained very competitive results on six benchmarks for three time series applications including a
satellite telemetry one. In fact, the proposed detector achieved a higher
precision (90.75%) and a slightly lower recall (95.30%) than TranAD
for the SMAP-MSL benchmark dataset.
2.1. Vanilla transformer
2.4. MEMTO: Memory-guided transformer for multivariate time series
anomaly detection
The vanilla Transformer was proposed by Vaswani et al. [32] as a
sequence-to-sequence model. It consists of three key modules to define
its embedding: Encoder–Decoder, the attention mechanism, and the
positional encoding. The Encoder and decoder sections are formed
by a stack of six identical layers. Each layer includes two sub-layers.
The first sub-layer is composed of a multi-head self-attention module
while the second one is a position-wise feed-forward network. To
make the training process more efficient, the encoder block employs
an additional residual connection followed by Layer Normalization
module. In comparison with the encoder, the decoder blocks add a
third sub-layer between the multi-head self-attention modules and the
position-wise. The supplementary sub-layer includes cross-attention
modules. To avoid each position from appearing at future positions,
additional self-attention modules are modified in the decoder.
The Anomaly Transformer [23] suffers from an overfitting issue,
which occurs when the anomalous patterns are reconstructed too well.
MEMTO [34] is a memory-guided Transformer designed by Song et al.
to solve the over generalization problem of the Anomaly Transformer
model by integrating memory networks into the Transformer encoder–
decoder architecture. MEMTO integrates an incremental memory updating process in the gated memory module.
To train each item in the gated memory module, MEMTO uses an
incremental technique. Based on the input data, it calculates the extent
to which each existing item has to be modified.
This procedure allows to determine the level to which each memory
item should be updated based on the input data injected into the
current memory items. This helps to fine-tune diverse normal patterns
in a more data-driven way. In addition to a bi-dimensional deviationbased detection criterion used to compute anomaly scores based on
both input space and latent space, MEMTO employs a two-phase training prototype which applies the K-means clustering to set up memory
items. This enhances the stability of the training procedure.
2.2. TranAD: Deep transformer networks for anomaly detection
TranAD [33] is a deep encoder–decoder transformer structure designed in 2022 by Tuli et al. to detect and diagnose anomalies in time
740
Acta Astronautica 238 (2026) 739–745
A. Fejjari et al.
Table 1
The number of telemetry samples in the training and test set with
ESA OPS-SAT dataset.
Table 2
The number of anomalies in the whole, training and test set, with ESA OPSSAT dataset.
Channel
Training samples
Test samples
Channel
Sum of anomalies
Anomaly training set
Anomaly test set
CADC0872
CADC0873
CADC0874
CADC0884
CADC0886
CADC0888
CADC0890
CADC0892
CADC0894
51,081
51,628
40,781
9121
241
8790
464
36,848
26,224
15,738
16,661
17,938
2689
132
2850
85
12,934
9288
CADC0872
CADC0873
CADC0874
CADC0884
CADC0886
CADC0888
CADC0890
CADC0892
CADC0894
24,366
22,449
35,444
0
87
2754
402
7234
7528
18,363
16,224
22,901
0
51
2344
317
5912
5367
6003
6225
12,543
0
36
410
85
1322
2161
Total
225,178
78,315
Total
100,264
71,479
28,785
Table 3
Details about implemented parameters.
MEMTO was evaluated on five real-world time series datasets of
different domains, including spacecraft telemetry monitoring. It gave
an average anomaly detection precision of 92.92% and a recall of
98.20% for the NASA SMAP-MSL dataset significantly outperforming
the other baselines.
Parameters
TranAD
Anomaly Transformer
MEMTO
Anomaly ratio
Number of epochs
Batch size
Windows size
–
20
256
10
1.0
10
256
100
1.0
100
256
100
3. Experiments
hidden units in encoder layers etc.) were the same as those in the
original transformer model papers.
For both Anomaly Transformer and MEMTO models, we used a
sliding window technique as a post-processing method to filter spikes.
The adopted technique uses the Savitzky–Golay smoothing filter [40].
We consider three quality metrics to measure and evaluate the performance of Transformer-based methods, namely precision (1), recall
(2), and 𝐹1 score (3). The inference time was also used to assess the
performance of AD approaches for potential onboard deployment.
In this section, we evaluate and compare the performance of different Transformer models in detecting anomalies for ESA-OPS-SAT
satellite telemetry data. We start by describing the benchmark ESAOPS-SAT. Then, we present evaluation metrics and used parameters.
Finally, we discuss and compare the results obtained.
3.1. ESA OPS-SAT dataset
In this work, we used the public dataset ESA OPS-SAT [35,36] to
evaluate the different Transformer models. The ESA OPS-SAT is a 2night-long telemetry dataset, collected by the ESA (European Space
Agency) on board the OPS-SAT satellite during the CubeSat mission in May 2024. The telemetry data contain 9 channels. Three
of them are related to the magnetometer parameters (CADC0872,
CADC0873, CADC0874), while the rest correspond to photo-diode
readouts (CADC0884, CADC0886, CADC0888, CADC0890, CADC0892,
CADC0894).
ESA OPS-SAT includes around 303,493 telemetry values split into
2123 segments. 1689 of these segments are labeled as normal, and 434
are annotated as anomalous telemetry fragments.
75% of the ESA OPS-SAT telemetry samples are reserved for training
and only 25% are used in the test set. The training-test dataset split performed in this work was predefined by ESA space operations engineers
and machine learning experts [35].
Table 1 presents the number of training and test samples for the
ESA OPS-SAT dataset. Table 2 shows the number of anomalies in each
channel. Fig. 1 presents an example of the distribution of anomalies
across the entire channel (Channel CADC0874), as well as the training
and test set.
𝑃 𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 =
𝑅𝑒𝑐𝑎𝑙𝑙 =
𝑇𝑃
𝑇𝑃 + 𝐹𝑃
𝑇𝑃
𝑇𝑃 + 𝐹𝑁
(1)
(2)
𝑃 𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 × 𝑅𝑒𝑐𝑎𝑙𝑙
(3)
𝑃 𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 + 𝑅𝑒𝑐𝑎𝑙𝑙
Where TP and FP present true positive and false positive samples,
respectively. FN and TN be regarded as to the false negative and true
negative samples, respectively.
𝐹1 𝑠𝑐𝑜𝑟𝑒 = 2 ×
3.3. Results and discussions
The performance results of the three different models described in
Section 2 are shown in Table 4. To study the influence of channel
length on AD results, we first generate the results for each channel
separately. Then, we produce results for the entire dataset. We highlight
the best results in bold. From this table, it can be seen that the
three implemented detectors all achieve good results in detecting true
positives (94%–98%), and they offer a low false alarm rate (i.e. false
positives). However, the TranAD model suffers more in terms of false
negatives.
The TranAD model, which uses a self-conditioning process to extract
short-term temporal trends, presents the lowest capability to detect
true anomalies, resulting in a recall of only 69.24%. The Anomaly
Transformer and MEMTO models exhibit a better precision, with reduced false alarms, compared to Tran-AD. They achieved 99.98% and
98.96% of precision respectively, and they managed to obtain a recall
of 97.52%–97.99%.
Although the three transformers models perform very well and show
competitive results, Transformer-based AD models encounter some
challenges. Among the limitations of Transformers is the inability to
capture anomalous behaviors with limited training data [41]. In fact,
Transformers models require a significant amount of data for training
3.2. General setup and evaluation metrics
We used the same code performed by the original Transformers
authors in [37–39] to evaluate the three detectors (TranAD, Anomaly
Transformer and MEMTO). The various Transformer models were
implemented using Python 3.8. The Data Science Research Platform
(DSRP) GPU computing cluster of the University of Malta with GeForce
RTX 2080 Ti GPUs were used to train the models and then to use these
models to detect anomalies in the test set. The three transformer models
were trained using the configuration listed in Table 3. A small window
size = 10 was used for short channels (CADC0886, CADC08890).
All other parameters (e.g. number of layers in transformer encoders,
741
Acta Astronautica 238 (2026) 739–745
A. Fejjari et al.
Table 4
Performance comparison between Transformer-based methods. A channel-bychannel breakdown is provided, as well as the results achieved on the full
dataset.
Model
TranAD
Anomaly
Transformer
MEMTO
Channel
Precision (%)
Recall (%)
𝐹1 score (%)
CADC0872
CADC0873
CADC0874
CADC0884
CADC0886
CADC0888
CADC0890
CADC0892
CADC0894
ESA OPS-SAT
93.78
96.05
98.74
0
0
77.94
0
71.15
70.46
94.24
75.87
46.57
87.81
0
0
49.99
0
47.57
29.15
69.24
83.88
62.72
92.96
0
0
60.92
0
57.02
41.24
79.82
CADC0872
CADC0873
CADC0874
CADC0884
CADC0886
CADC0888
CADC0890
CADC0892
CADC0894
ESA OPS-SAT
99.95
100
99.98
0
0
99.53
0
100
99.95
99.98
93.10
96.76
99.60
0
0
51.53
0
100
100
97.52
96.40
98.35
99.79
0
0
68.27
0
100
99.98
98.73
CADC0872
CADC0873
CADC0874
CADC0884
CADC0886
CADC0888
CADC0890
CADC0892
CADC0894
ESA OPS-SAT
99.88
99.88
99.98
0
100
99.61
100
99.92
99.40
98.96
97.18
98.04
99.99
0
97.22
62.93
98.75
97.96
100
97.99
98.51
98.95
99.99
0
98.59
77.13
99.37
98.93
99.70
98.96
this model shows poor efficiency to capture anomalies in short-term
channels (CADC0886 and CADC0890), which have a limited number
of training samples.
The experimental results provide strong evidence supporting the
effectiveness of the two-phase training paradigm of MEMTO in detecting anomalies with channels that have a small quantity of samples. In
fact, MEMTO succeeded in capturing anomalies in different channels,
including the short ones. For example, MEMTO obtains a recall of
97.22% and 98.75% for the two channels CADC0886 and CADC0890,
respectively.
We can see an example of anomaly detection results for two OPSSAT telemetry channels in Figs. 2 and 3. Fig. 2 shows the anomalies
detected by the three Transformer-based models on a short channel
(CADC0886). Fig. 3 presents an example of the results obtained in
a long telemetry channel (CADC08874). We observe that MEMTO
can detect the different types of anomaly sequences identified in the
ground truth in both short and long telemetry channels. Furthermore,
it presents a high ability to capture complex and difficult time series
sequences. Anomalies in long channels are more easily detected for the
two other detectors (TranAD and Transformer Anomaly) than for short
channels.
Several research studies have shown that transformer-based models
suffer from interpretability challenges [42,43]. In fact, Transformers
can be difficult to interpret, making it challenging to understand why
they make specific predictions. This could be the explanation for the
results obtained using channel CADC0884.
To assess the performance of Transformer-based models over the
best performing baseline detectors used in the ESA OPS-SAT benchmark paper [35], we compared the obtained results with four other
supervised/unsupervised AD algorithms, which are: FCNN (Fully Connected Neural Network with dropout and batch normalization) [44],
XGBOD (Extreme Gradient Boosting Outlier Detection) [45], SO-GAAL
(Single-objective Generative Adversarial Active Learning) and MOGAAL (Multi-objective Generative Adversarial Active Learning) [46].
Fig. 1. An example of normalized time-series data and their anomalies sequence, identified on channel CADC0874: (a) whole, (b) train and (c) test
channel data.
to detect anomalies. This could explain the low performance results
that were obtained with channels that included a limited number of
samples (CADC0886 and CADC0890).
Due to the model-agnostic meta learning (MAML) process, the experiments performed with the TranAD in [33] have shown that this
model could be trained using only 20% of the data. However, the current study shows that TranAD does not perform well with channels that
were trained with limited data. The quantitative results provided by
Anomaly Transformer for the two channels CADC0886 and CADC0890
confirm the same viewpoint. We note that Anomaly Transformer has a
high ability to process long-term and medium-term channels. However,
742
Acta Astronautica 238 (2026) 739–745
A. Fejjari et al.
Fig. 2. The result of the three transformer-based models on channel CADC0886 from the OPS-SAT dataset.
Fig. 3. The result of the three transformer-based models on channel CADC0874 from the OPS-SAT dataset.
Table 5 summarizes the results obtained using all the investigated AD
algorithms on the complete OPS-SAT dataset.
to supervised AD methods. The two detectors made an 𝐹1 score improvement between 4 pp and 7 pp compared to FCNN and XGBOD,
respectively.
The inference times in microseconds (μs) per sample for each channel for the three Transformer models are shown in Table 6. TranAD
exhibits fastest inference time, while the inference time is slowest
for MEMTO. TranAD’s ability to detect anomalies faster than other
detectors is due to the use of smaller windows, as smaller inputs require
less inference time.
We have compared the inference time of the three Transformer
models with two other DL models which are the CNN AutoEncoder [47]
and RNN LSTM [48]. The obtained results confirm that the Transformers models allow much faster detection compared to the other
The precision of the Transformer models has been further improved
compared to the literature approaches. For example, MEMTO saw an
enhancement of up to 8.36 percentage points (pp) against the SO GAAL
method (90.60-98.96) and 3.8 pp compared to XGBOD (90.60-94.4).
Besides, the three Transformer detectors outperform the supervised algorithms in minimizing false negatives. For instance, TranAD exceeded
the state-of-the-art SO/MO GAAL models in detecting true anomalies.
The recall rate went up from 51.3% to 69.24%, which was better than
SO GAAL. Further, we observe that MEMTO and Anomaly Transformer
are both able to achieve superior anomaly detection results compared
743
Acta Astronautica 238 (2026) 739–745
A. Fejjari et al.
Table 5
Performance comparison of Transformer-based models with reference algorithms on the complete OPS-SAT dataset.
Model
Precision (%)
Recall (%)
𝐹1 score (%)
96.3
94.4
92.9
89.4
94.6
91.8
98.5
90.6
94.24
98.98
98.96
57.5
51.3
69.24
97.52
97.99
72.6
65.5
79.82
98.73
98.96
inference time. This makes Transformers an appropriate choice for
satellite systems that require accurate and fast anomaly detection.
Our future work would involve implementing these models in hardware typical of an onboard installation to determine whether real-time,
onboard anomaly detection is feasible. We are also interested in improving the transformers performance results using limited training
data.
Supervised algorithms
FCNN
XGBOD
Unsupervised algorithms
MO-GAAL
SO-GAAL
TranAD
Anomaly Transformer
MEMTO
CRediT authorship contribution statement
Asma Fejjari: Writing – original draft, Visualization, Software,
Methodology, Formal analysis, Data curation, Conceptualization. Alexis
Delavault: Writing – review & editing. Robert Camilleri: Writing
– review & editing, Validation, Supervision, Methodology. Gianluca
Valentino: Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Formal analysis.
Table 6
Comparison of inference time in microseconds (μs) per sample, between
Transformer-based methods with baseline methods.
Declaration of competing interest
Channel
AutoEncoder
LSTM
Time (μs)
TranAD
Anomaly Transformer
MEMTO
CADC0872
CADC0873
CADC0874
CADC0884
CADC0886
CADC0888
CADC0890
CADC0892
CADC0894
2568
2315
2009
2615
305 060
2219
28 459
1880
2445
30 023
27 215
25 993
32 595
4 039 638
27 590
1 707 540
112 800
146 700
98.1
93.2
80.9
98.3
1065.3
83.4
510
89.6
82.79
1000
1000
800
3200
42 000
2500
63 000
1100
1200
1100
1000
1000
5400
114 000
5600
168 000
1300
1700
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Acknowledgment
The ASTRA-AI project is supported and financed by Xjenza Malta
through the FUSION Space Upstream Program.
References
Table 7
Performance comparison between Transformer-based methods in NASA
SMAP/MSL and ESA OPS-SAT complete datasets.
Model
Dataset
Precision (%)
Recall (%)
𝐹1 score (%)
TranAD
NASA SMAP
NASA MSL
ESA OPS-SAT
80.43
90.38
94.24
99.99
99.99
69.24
89.15
94.94
79.82
NASA SMAP
NASA MSL
ESA OPS-SAT
94.13
92.09
99.98
99.40
95.15
97.52
96.69
93.59
98.73
NASA SMAP
NASA MSL
ESA OPS-SAT
93.76
92.07
98.96
99.63
96.76
97.99
96.61
94.76
98.96
Anomaly
Transformer
MEMTO
[1] D. Pan, Z. Song, L. Nie, B. Wang, Satellite telemetry data anomaly detection using
bi-LSTM prediction based model, in: 2020 IEEE International Instrumentation and
Measurement Technology Conference (I2MTC), 2020, pp. 1–6, http://dx.doi.org/
10.1109/I2MTC43012.2020.9129010.
[2] H. Jiang, K. Zhang, J. Wang, X. Wang, P. Huang, Anomaly detection and
identification in satellite telemetry data based on pseudo-period, Appl. Sci. 10
(1) (2020) http://dx.doi.org/10.3390/app10010103.
[3] B. El Habib, B. Nasri, Detection and prediction of satellite telemetry anomalies, in: 2024 4th International Conference on Embedded & Distributed
Systems (EDiS), 2024, pp. 310–313, http://dx.doi.org/10.1109/EDiS63605.2024.
10783288.
[4] S. Abdelghafar, A. Darwish, A.E. Hassanien, M. Yahia, A. Zaghrout, Anomaly
detection of satellite telemetry based on optimized extreme learning machine, J.
Space Saf. Eng. 6 (4) (2019) 291–298, http://dx.doi.org/10.1016/j.jsse.2019.10.
005.
[5] N. Gu, K. Liu, W. Wu, X. Guo, An anomaly detection methodology
of load-carrying structure of satellite based on high-precision predictive
model, Acta Astronaut. 218 (2024) 330–341, http://dx.doi.org/10.1016/j.
actaastro.2024.02.042, URL: https://www.sciencedirect.com/science/article/pii/
S0094576524001140.
[6] L. Schlag, C. O’Meara, M. Wickler, Numerical analysis of automated anomaly
detection algorithms for satellite telemetry, 2018, http://dx.doi.org/10.2514/6.
2018-2534.
[7] S. Katsube, H. Sahara, Telemetry monitoring system with features explaining
anomalies based on mahalanobis distance, Int. J. Progn. Heal. Manag. 15 (2024)
http://dx.doi.org/10.36001/ijphm.2024.v15i2.3945.
[8] Z. Xu, Z. Cheng, B. Guo, A hybrid data-driven framework for satellite telemetry
data anomaly detection, Acta Astronaut. 205 (2023) 281–294, http://dx.doi.org/
10.1016/j.actaastro.2023.02.009.
[9] Z.P. Xu, Z.J. Cheng, J.H. He, B. Guo, A hybrid model for detecting satellite
telemetry data anomalies, in: 2022 13th International Conference on Reliability,
Maintainability, and Safety, ICRMS, 2022, pp. 180–184, http://dx.doi.org/10.
1109/ICRMS55680.2022.9944601.
[10] B. Ruszczak, K. Kotowski, J. Andrzejewski, A. Musiał, D. Evans, V. Zelenevskiy,
S. Bammens, R. Laurinovics, J. Nalepa, Machine learning detects anomalies
in OPS-sat telemetry, in: J. Mikyška, C. de Mulatier, M. Paszynski, V.V.
Krzhizhanovskaya, J.J. Dongarra, P.M. Sloot (Eds.), Computational Science –
ICCS 2023, Springer Nature Switzerland, Cham, 2023, pp. 295–306.
[11] L. Cui, Q. Zhang, Y. Shi, L. Yang, Y. Wang, J. Wang, C. Bai, A method for satellite
time series anomaly detection based on fast-dtw and improved-KNN, Chin. J.
Aeronaut. 36 (2) (2023) 149–159, http://dx.doi.org/10.1016/j.cja.2022.05.001.
[12] Y. Peng, S. Jia, X. Feng, F. Su, Telemetry fault detection for meteorological satellite based on PCA, in: 2016 16th International Symposium on Communications
and Information Technologies, ISCIT, 2016, pp. 357–361, http://dx.doi.org/10.
1109/ISCIT.2016.7751650.
implemented methods. For example, LSTM requires more than 25 times
the inference time of MEMTO, for the long channels (CADC0872,
CADC0873, CADC0874).
Table 7 shows the AD performance results of the Transformers
models on three real-world datasets. Overall, MEMTO and Anomaly
Transformer models consistently show high performance compared to
the TranAD detector on all datasets.
4. Conclusions
In this work, we present and compare for the first time three
Transformer-based anomaly models, namely TranAD, Anomaly Transformer and MEMTO. The three transformer methods were explored
to detect and diagnose anomalies in a new satellite telemetry dataset
called ESA OPS-SAT. Extensive experiments on the ESA OPS-SAT
dataset demonstrate that the three Transformer neural networks exhibit
robust performance in detecting anomalous patterns. They achieve
much better performance compared to other supervised and unsupervised models such as FCNN and SO/MO GAAL. Specifically, MEMTO
and Anomaly Transformer managed to correctly identify 97.99% and
97.52% of true anomalies respectively, higher than the state-of-the-art
models. Moreover, the different Transformer encoder–decoder architectures allow identifying data trends with significant variations in
744
Acta Astronautica 238 (2026) 739–745
A. Fejjari et al.
[13] Z. Xu, Z. Cheng, Q. Tang, B. Guo, An encoder-decoder generative adversarial
network-based anomaly detection approach for satellite telemetry data, Acta
Astronaut. 213 (2023) 547–558, http://dx.doi.org/10.1016/j.actaastro.2023.09.
032.
[14] C. OMeara, L. Schlag, M. Wickler, Applications of deep learning neural networks
to satellite telemetry monitoring, in: 2018 SpaceOps Conference, http://dx.doi.
org/10.2514/6.2018-2558.
[15] Z. Zeng, J. Lei, G. Jin, C. Xu, L. Zhang, Detecting anomalies in satellite telemetry
data based on causal multivariate temporal convolutional network, in: 2022 IEEE
5th International Conference on Big Data and Artificial Intelligence, BDAI, 2022,
pp. 63–74, http://dx.doi.org/10.1109/BDAI56143.2022.9862616.
[16] H. Zhao, S. Qiu, J. Yang, J. Guo, M. Liu, X. Cao, Satellite early anomaly detection
using an advanced transformer architecture for non-stationary telemetry data,
IEEE Trans. Consum. Electron. 70 (1) (2024) 4213–4225, http://dx.doi.org/10.
1109/TCE.2024.3357881.
[17] Y. Wang, J. Gong, J. Zhang, X. Han, A deep learning anomaly detection
framework for satellite telemetry with fake anomalies, Int. J. Aerosp. Eng. 2022
(1) (2022) 1676933, http://dx.doi.org/10.1155/2022/1676933.
[18] J. Wang, H. Li, L. Wang, Z. Xu, Satellite telemetry data anomaly detection
using multiple factors and co-attention based LSTM, in: 2023 IEEE Wireless
Communications and Networking Conference, WCNC, 2023, pp. 1–6, http://dx.
doi.org/10.1109/WCNC55385.2023.10118903.
[19] Q. Wen, T. Zhou, C. Zhang, W. Chen, Z. Ma, J. Yan, L. Sun, Transformers in time
series: a survey, IJCAI ’23, 2023, http://dx.doi.org/10.24963/ijcai.2023/759.
[20] Y. Hou, H. Li, Y. Wang, L. Wang, Z. Xu, Satellite anomaly detection based on
improved transformer method, in: 2023 24st Asia-Pacific Network Operations
and Management Symposium, APNOMS, 2023, pp. 322–325.
[21] J. Kim, H. Kang, P. Kang, Time-series anomaly detection with stacked transformer
representations and 1D convolutional network, Eng. Appl. Artif. Intell. 120
(2023) 105964, http://dx.doi.org/10.1016/j.engappai.2023.105964.
[22] Y. Jeong, E. Yang, J.H. Ryu, I. Park, M. Kang, Anomalybert: Self-supervised
transformer for time series anomaly detection using data degradation scheme,
2023, arXiv preprint arXiv:2305.04468.
[23] J. Xu, H. Wu, J. Wang, M. Long, Anomaly transformer: Time series anomaly
detection with association discrepancy, 2021, ArXiv abs/2110.02642. URL: https:
//api.semanticscholar.org/CorpusID:238408395.
[24] G. Zerveas, S. Jayaraman, D. Patel, A. Bhamidipaty, C. Eickhoff, A transformerbased framework for multivariate time series representation learning, in:
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &
Data Mining, KDD ’21, Association for Computing Machinery, New York, NY,
USA, 2021, pp. 2114–2124, http://dx.doi.org/10.1145/3447548.3467401.
[25] H. Meng, Y. Zhang, Y. Li, H. Zhao, Spacecraft anomaly detection via transformer
reconstruction error, in: Z. Jing (Ed.), Proceedings of the International Conference on Aerospace System Science and Engineering 2019, Springer Singapore,
Singapore, 2020, pp. 351–362.
[26] I. Sutskever, O. Vinyals, Q.V. Le, Sequence to sequence learning with neural
networks, in: Proceedings of the 27th International Conference on Neural
Information Processing Systems - Volume 2, NIPS ’14, MIT Press, Cambridge,
MA, USA, 2014, pp. 3104–3112.
[27] A. Gulati, J. Qin, C.-C. Chiu, N. Parmar, Y. Zhang, J. Yu, W. Han, S. Wang,
Z. Zhang, Y. Wu, R. Pang, Conformer: Convolution-augmented transformer for
speech recognition, 2020, URL: https://arxiv.org/abs/2005.08100. arXiv:2005.
08100.
[28] T. Xiao, J. Zhu, Introduction to transformers: an NLP perspective, 2023, URL:
https://arxiv.org/abs/2311.17633. arXiv:2311.17633.
[29] B. Song, B. Guo, W. Hu, Z. Zhang, N. Zhang, J. Bao, J. Wang, J. Xin, Transformerbased time-series forecasting for telemetry data in an environmental control and
life support system of spacecraft, Electronics 14 (3) (2025) http://dx.doi.org/10.
3390/electronics14030459.
[30] E. Eldele, M. Ragab, Z. Chen, M. Wu, X. Li, Tslanet: Rethinking transformers for
time series representation learning, in: ICML, 2024, URL: https://openreview.
net/forum?id=CGR3vpX63X.
[31] A. Fejjari, A. Delavault, R. Camilleri, G. Valentino, A review of anomaly detection
in spacecraft telemetry data, Appl. Sci. 15 (10) (2025) http://dx.doi.org/10.
3390/app15105653, URL: https://www.mdpi.com/2076-3417/15/10/5653.
[32] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, L.u.
Kaiser, I. Polosukhin, Attention is all you need, in: I. Guyon, U.V. Luxburg, S.
Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett (Eds.), in: Advances
in Neural Information Processing Systems, vol. 30, Curran Associates, Inc., 2017.
[33] S. Tuli, G. Casale, N.R. Jennings, Tranad: deep transformer networks for anomaly
detection in multivariate time series data, Proc. VLDB Endow. 15 (6) (2022)
1201–1214, http://dx.doi.org/10.14778/3514061.3514067.
[34] J. Song, K. Kim, J. Oh, S. Cho, MEMTO: Memory-guided transformer for
multivariate time series anomaly detection, in: Thirty-Seventh Conference on
Neural Information Processing Systems, 2023, URL: https://openreview.net/
forum?id=UFW67uduJd.
[35] B. Ruszczak, K. Kotowski, D. Evans, J. Nalepa, The OPS-sat benchmark for
detecting anomalies in satellite telemetry, 2024, arXiv preprint arXiv:2407.
04730.
[36] R. Bogdan, OPSSAT-AD - anomaly detection dataset for satellite telemetry, 2024,
http://dx.doi.org/10.5281/zenodo.12588359, (Accessed 15 October 2024).
[37] S. Tuli, TranAD, 2024, https://github.com/imperial-qore/TranAD. (Accessed 23
August 2024).
[38] H. Wu, Anomaly-Transformer, 2024, https://github.com/thuml/AnomalyTransfURLormer. (Accessed 10 November 2024).
[39] S.J. Ho, MEMTO, 2025, https://github.com/Song-Jun-Ho/MEMTO/tree/main.
(Accessed 19 January 2025).
[40] R.W. Schafer, What is a savitzky-golay filter? [lecture notes], IEEE Signal Process.
Mag. 28 (4) (2011) 111–117, http://dx.doi.org/10.1109/MSP.2011.941097.
[41] S. Khanal, S. Tirupathi, G. Zizzo, A. Rawat, T.B. Pedersen, Domain adaptation
for time series transformers using one-step fine-tuning, 2024, URL: https://arxiv.
org/abs/2401.06524. arXiv:2401.06524.
[42] G. Jeanneret, L. Simon, F. Jurie, Disentangling visual transformers: Patch-level
interpretability for image classification, 2025, URL: https://arxiv.org/abs/2502.
17196. arXiv:2502.17196.
[43] M. Denizoglu, A Systematic Review of Explainable AI Methods for Transformers
in Software Engineering, 2024.
[44] D. Kwon, H. Kim, J. Kim, S.C. Suh, I. Kim, K.J. Kim, A survey of deep learningbased network anomaly detection, Clust. Comput. 22 (2017) 949–961, URL:
https://api.semanticscholar.org/CorpusID:4505261.
[45] Y. Zhao, M.K. Hryniewicki, XGBOD: Improving supervised outlier detection with
unsupervised representation learning, in: 2018 International Joint Conference on
Neural Networks, IJCNN, 2018, pp. 1–8, http://dx.doi.org/10.1109/IJCNN.2018.
8489605.
[46] Y. Liu, Z. Li, C. Zhou, Y. Jiang, J. Sun, M. Wang, X. He, Generative adversarial
active learning for unsupervised outlier detection, IEEE Trans. Knowl. Data Eng.
32 (8) (2020) 1517–1528, http://dx.doi.org/10.1109/TKDE.2019.2905606.
[47] A.A. Neloy, M. Turgeon, A comprehensive study of auto-encoders for anomaly
detection: Efficiency and trade-offs, Mach. Learn. Appl. 17 (2024) 100572,
http://dx.doi.org/10.1016/j.mlwa.2024.100572.
[48] Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (7553) (2015)
436–444.
745