Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey

Ammar Bouketta; Smail Niar; Hamza Ouarnoughi

cs.LGSep 30, 2026

Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey

Authors: Ammar Bouketta, Smail Niar, Hamza Ouarnoughi

Organizations: Université Polytechnique Hauts-de-France, LAMIH CNRS UMR 8201, Valenciennes, France · Alstom, Crespin, France · Université Polytechnique Hauts-de-France, LAMIH CNRS UMR 8201, INSA Hauts-de-France, Valenciennes, France · Computer Science Department, College of Computing and Informatics, University of Sharjah, Sharjah, UAE

Abstract

Ensuring safe and reliable operation of modern railway systems increasingly relies on data-driven monitoring and intelligent fault detection. Deep learning has emerged as an effective paradigm for railway anomaly detection, driven by the growing availability of heterogeneous sensor data from rolling stock and infrastructure. This paper presents a structured survey of deep learning-based anomaly detection approaches for railway systems. The surveyed methods are organized using a unified taxonomy covering anomaly location, data representation and manifestation, sensing modality, and temporal characteristics. Existing approaches, including convolutional, recurrent and attention-based architectures, autoencoders, generative adversarial networks, and transformers, are structured into classification-based, prediction-based, reconstruction-based, and hybrid learning paradigms. The survey also examines data-centric challenges, evaluation practices, performance metrics, and practical deployment aspects, including edge-cloud architectures, computational constraints, and hardware-aware optimization. Finally, a decision-oriented framework links anomaly characteristics, data properties, and operational constraints to suitable detection paradigms and deployment configurations. This work provides a structured reference for selecting and deploying deep learning solutions for railway anomaly detection and highlights open challenges toward reliable and scalable intelligent monitoring systems.

Figures & tables

Figure 1: Overview of railway subsystems and anomaly detection domains addressed in this survey, organized into three levels: infrastructure, rolling stock, and environmental or behavioral contexts.

Figure 2: PRISMA-inspired flow diagram of the literature identification, screening, eligibility, and inclusion process adopted in this survey.

Survey	Year	Infra.	Train	Env.	DL Strat.	Data Chal.	Depl.
Oh et al. [ 100 ]	2022	✓	✓	✓	✓	✗	NT
Lourenço et al. [ 90 ]	2024	✓	✓	✗	✓	✓	NT
Tang et al. [ 137 ]	2022	✓	✓	✓	✓	✗	NT
Kumar et al. [ 78 ]	2024	✓	✓	✗	✗	✗	NT
De Donato et al. [ 39 ]	2022	✓	✗	✗	✓	✗	T
Di Summa et al. [ 41 ]	2023	✓	✗	✗	✗	✗	NT

Table 1: Comparative Overview of Survey Coverage in Railway AI. Infra: infrastructure, Env: environmental, DL Strat: DL Strategy, Data Chal: Data Challenges, Depl: Deployment.

Figure 3: Four-axis taxonomy for railway anomaly characterization: location of occurrence, manifestation in data, sensing modality, and temporal behavior.

Ref.	Dataset name	Acc.	System	Modality	Scale (samples)	Labeled	Fault Types
[ 129 ]	Track-Geom-Defect	R	Railway track	Track geometry	172 436	Yes	Rail, switch, crossing defects
[ 122 ]	Rail-Acoustic	R	Track, fasteners	Audio signals	1625	Yes	Wheel burn, loose nut-bolt
[ 73 ]	IRJ-SparkVision	R	Rail joints (IRJ)	RGB images	$\sim$ 28 150	Yes	Spark erosion, rail damage
[ 156 ]	Drone Fastener	R	Rail fasteners	RGB images	500	Yes	Missing fastener
[ 107 ]	Track Fault	P	Rails, fasteners	RGB images	293	Partial	Cracked rails, rusted bolts
[ 52 ]	Fastener Vision	R	Rail fasteners	RGB + depth	3 300	Yes	Elastic strip, Nut, Baffle miss

Table 2: Infrastructure-Level Railway Datasets. Accessibility(Acc.): Public (P), Restricted (R). NR = not reported.

Ref.	Dataset name	Acc.	System	Modality	Scale (samples)	Labeled	Fault Types
[ 87 ]	HSR-Sim	R	Railway electrification system	Multivariate signals	103 200	Yes	Short-circuit fault
[ 61 ]	Train-DoorRig	R	Train doors	Current	626	Yes	Bearing wear, obstruction
[ 49 ]	RailHVAC-RUL	R	HVAC system	Multivariate signals	NR	Yes	Air filter clogging
[ 133 ]	PlugDoor Sound	R	Sliding doors	Acoustic signals	164	Yes	Door faults
[ 141 ]	Braking System	R	Braking system	Multivariate signals	28996	Yes	Pressure, braking force deviation
[ 120 ]	Wheel Flat	P	Wheel-rail	Spectrogram images	4977	Yes	Wheel flat detection

Table 3: Rolling Stock-Level Railway Datasets. Accessibility(Acc.): Public (P), Restricted (R). NR = not reported.

Ref.	Dataset name	Acc.	System	Modality	Scale (samples)	Labeled	Fault Types
[ 146 ]	RFOD Track	R	Track surroundings	RGB images	7235	Partial	Foreign objects, obstacles
[ 98 ]	Foreign Object	R	Track area	RGB images	2534	Yes	Pedestrians, falling rocks
[ 85 ]	HSR Strong-Wind	R	Wind-exposed track	Meteoro-logical signals	NR	Yes	Wind-induced speed restriction events

Table 4: Environmental and Behavioral-Level Railway Datasets for Anomaly Detection. Accessibility(Acc.): Public (P), Restricted (R). NR = not reported.

Figure 4: Typical pipeline for classification-based anomaly detection in railway systems.

Ref.	Year	System	Architecture	Modality	Metric	AD Task
[ 113 ]	2021	Rail track	SVM, RF, MLP	Acoustic	Acc.: 97%	Classify track faults (wheel burn, loose bolt) from acoustic signals.
[ 133 ]	2020	Train doors	SVM (+MFCC features)	Acoustic	Acc.: $>$ 95%	Classify plug-door fault types from acoustic measurements.
[ 49 ]	2021	HVAC system	Gradient Boosted Trees (GBT)	Multivariate	Acc.: 92.2%	Detect air filter faults in railway HVAC from sensor data.
[ 122 ]	2022	Rail track	MLP + MFCC	Acoustic	Acc.: 98.4%	Classify rail structural faults from IoT acoustic data.
[ 61 ]	2019	Train doors	KNN vs. CNN	Current	Acc.: 99.5%	Detect abnormal current patterns in train door motors.
[ 111 ]	2025	Rail track	SVM, GRU, LSTM	Acoustic	Acc.: $>$ 96%	Classify track faults using combined MFCC and CQT features.

Table 5: Classical Machine Learning classifiers for railway anomaly detection. All methods are supervised. Acc.: Accuracy.

Ref.	Year	System	Architecture	Modality	Metric	AD Task
[ 129 ]	2022	Track geometry	CNN, DNN	Track geometry signals	Acc.: 94.3%	Classify rail geometry defects.
[ 107 ]	2022	Rail surface	ResNet-50, VGG-16	RGB images	Acc.: 83.8%	Detect surface defects (cracks, corrosion).
[ 13 ]	2024	Rail and wheel	Ensemble CNN	RGB images	Acc.: 99%	Classify rail and wheel surface defects.
[ 109 ]	2024	Rail track	YOLOv11 + TL(Transfer Learning)	RGB images	Acc.: 92.5%	Binary classification of track images: defective vs. normal.
[ 52 ]	2023	Rail fasteners	CSP-Darknet53	RGB + Depth	mAP: 86.6%	Detect and localize missing and broken fasteners.
[ 73 ]	2025	Rail joints	SqueezeNet	RGB images	Acc.: 98.2%	Detect and segment spark erosion on insulated rail joints.

Table 6: Vision-based deep classifiers for railway anomaly detection. All methods are supervised. Acc.: Accuracy; mAP: mean Average Precision; mIoU: mean Intersection over Union; Prec.: Precision.

Ref.	Year	System	Architecture	Modality	Metric	AD Task
[ 120 ]	2023	Wheel-rail	LeNet-5 CNN	Vibration signals	Acc.: 97%, AUC: 0.97	Detect and classify wheel flat defects from vibration spectrograms.
[ 141 ]	2021	Braking system	Multivariate CNN	Pressure + force	Acc.: 93%	Classify braking faults from multivariate signals.
[ 87 ]	2020	Traction system	GRU hierarchical	Multivariate	Acc.: $>$ 93%	Classify 29 traction fault types from high-frequency sensor streams.
[ 112 ]	2023	Wheelset	1D-CNN, LSTM	Acceleration	NR	Detect and classify wheelset faults from raw acceleration time series.
[ 136 ]	2023	Passenger cabin	SlowFast CNN	Video + acoustic	Acc.: 85.1%	Classify abnormal passenger behavioral events.

Table 7: Time-series signal classifiers for railway anomaly detection. All methods are supervised. Acc.: Accuracy; AUC: Area Under the ROC Curve; NR: Not Reported.

Figure 5: Typical pipeline for prediction-based anomaly detection in railway systems.

Ref.	Year	System	Architecture	Modality	AD Task
[ 81 ]	2021	Rolling stock	LSTM, RNN+ DNN	Multivariate signals	Predict derailment risk and abnormal running behavior from onboard sensors.
[ 149 ]	2022	HVAC, compressors	Modified Long Short-Term Memory	Multivariate signals	Predict normal system state and flag anomalies in multiple rolling stock subsystems.
[ 157 ]	2021	Air conditioning unit	LSTM, RNN	Temperature + current	Detect anomalies in vehicle air conditioning units from monitoring data deviations.
[ 121 ]	2022	Air conditioning unit	LSTM	Current signals	Detect abnormal air conditioning units recorded in vehicule on operation.
[ 62 ]	2023	Track geometry	LSTM	Inertial signals	Forecast track geometry parameters to detect misalignment before safety limits are exceeded.
[ 108 ]	2023	Train traction converter cooling	LSTM	Multivariate signals	Predict rolling stock equipment failures hours in advance from sensor streams.

Table 8: Recurrent sequence predictors for railway anomaly detection. Models are trained on normal data only unless otherwise noted. F1: F1-score; Acc.: Accuracy; AUC: Area Under the ROC Curve; NR: not reported.

Ref.	Year	System	Architecture	Modality	AD Task
[ 85 ]	2021	High-speed rail wind monitoring	Multiple Attention Layer based Multi-Instance Learning (MAL-MIL)	Meteorological signals	Predict wind hazard probability in real-time along high-speed rail corridors.
[ 43 ]	2025	Axle bearing	Improved Empirical Wavelet Transform (IEWT)+CNN	Vibration signals	Predict bearing degradation and detect faults from vibration time series.

Table 9: Attention and Transformer predictors for railway anomaly detection. AUC: Area Under the ROC Curve; NR: not reported.

Ref.	Year	System	Architecture	Modality	AD Task
[ 50 ]	2022	Heating ventilation air conditioning filter	1D CNN + physics model	Multivariate signals	Estimate remaining useful life of air filter and detect degradation anomalies.
[ 59 ]	2024	Rail corrugation	1D CNN	Acceleration signals	Predict rail corrugation profile from vehicle acceleration to detect surface anomalies.
[ 163 ]	2024	Rail corrugation	Self-attention + TCN + GRU	Acceleration signals	Predict corrugation evolution trend and detect anomalous degradation progression.

Table 10: Convolutional temporal predictors for railway anomaly detection. Acc.: Accuracy; NR: not reported; TCN: Temporal Convolutional Network; GRU: Gated Recurrent Unit.

Figure 6: Typical pipeline for reconstruction-based anomaly detection in railway systems.

Ref.	Year	Subsystem	Architecture	Modality	AD Task
[ 37 ]	2021	Air production unit	SAE	Pressure + current	Detect anomalies in APU analog and digital sensor streams without fault labels.
[ 38 ]	2024	Air production unit	LSTM-AE vs. SAE	Pressure + current	Detect APU anomalies using temporal reconstruction on MetroPT-3 dataset.
[ 72 ]	2021	Braking system	One-class LSTM-AE	Current signals	Detect anomalies in metro brake operating unit from current signal reconstruction.
[ 35 ]	2024	Rolling stock	LSTM-AE	Multivariate signals	Online reconstruction-based anomaly detection in multivariate railway sensor streams.
[ 110 ]	2024	High-speed rail	Convolutional AE	Vibration signals	Detect seismic anomalies from train vibration using normal-only autoencoder reconstruction.
[ 159 ]	2021	Rail surface	CVAE	ABA signals	Detect rail squat defects as shallow as 0.03 mm from normal ABA signal reconstruction.

Table 11: Standard and recurrent autoencoder approaches for railway anomaly detection.

Ref.	Year	Subsystem	Architecture	Modality	AD Task
[ 146 ]	2022	Track obstacles	GAN (adversarial AE)	RGB images	Detect foreign objects on tracks by reconstructing normal track scenes from onboard cameras.
[ 142 ]	2024	Metro undercarriage	GAN + memory module	RGB images	Detect undercarriage anomalies via adversarial memory-enhanced image reconstruction.
[ 143 ]	2024	Metro tracks	CNN + Transformer	RGB images	Detect track surface anomalies by reconstructing normal track images with memory augmentation.
[ 164 ]	2025	Railway turnout	PatchCore + attention	RGB images	Detect visual anomalies in turnout environments using unsupervised feature reconstruction.
[ 155 ]	2024	Pantograph	Reverse distillation network	RGB images	Detect pantograph anomalies via unsupervised reverse distillation image reconstruction.

Table 12: Generative adversarial and transformer reconstruction models for railway anomaly detection. All methods are trained on normal data only. GAN: Generative Adversarial Network; CNN: Convolutional Neural Network.

Figure 7: Typical pipeline for hybrid anomaly detection in railway systems. Two or more detection paradigms process the input in parallel. Their outputs are fused into a unified anomaly score for the final decision.

Ref.	Year	Subsystem	Architecture	Modality	AD Task
[ 80 ]	2021	Train doors	Conditional GAN + U-Net (reconstruction + prediction)	Video	Detect hazardous door events by jointly reconstructing frames and predicting optical flow.
[ 51 ]	2020	Pantograph-catenary	YOLO + AE + LSTM (classification + reconstruction + prediction)	RGB + IR + UV + vibration	Detect pantograph anomalies across four modalities using parallel specialized detection modules.
[ 28 ]	2022	Track infrastructure	GAN + meta-learner (reconstruction + supervised)	RGB images	Detect track anomalies from limited labeled examples using generative reconstruction and meta-learning.
[ 132 ]	2025	Track obstacles	Normalizing flow + Transformer (unsupervised + supervised)	RGB images	Detect and localize anomalous objects using hybrid unsupervised backbone and supervised decoder.
[ 152 ]	2023	Track components	CNN + rule-based module (classification + rules)	RGB images	Inspect track components by combining learned classification with geometric domain rules.

Table 13: Hybrid approaches for railway anomaly detection. IR: Infrared; UV: Ultraviolet.

Task	Metric	What it measures	Use in railway / Limitation
Classification	Accuracy	Overall fraction of correct predictions.	Avoid: unreliable when faults represent $<$ 1% of data (a model predicting "normal" for every sample achieves 99% accuracy.)
	Recall	Fraction of actual faults correctly detected.	Prioritize in safety-critical scenarios (braking): a missed fault is more dangerous than a false alarm [ 115 ] .
	Precision	Fraction of detected anomalies that are genuine faults.	Use when false alarms are operationally costly (unnecessary maintenance stops).
	F1-score	Harmonic mean of precision and recall.	Preferred for imbalanced binary detection.
	Specificity	Fraction of normal samples correctly identified.	Use to control false alarm rates in continuous monitoring; complements recall.
Anomaly scoring (unsupervised)	AUROC	Ranking quality of anomaly scores across all thresholds.	Use for threshold-independent comparison; avoid when anomaly prevalence is very low (PR-AUC is preferable) [ 127 ] .

Table 14: Evaluation metrics for railway anomaly detection.

Metric	Studies	%
Accuracy	42	61.8%
Precision / Recall	38	55.9%
F1-score	36	52.9%
mAP / mIoU	14	20.6%
AUROC	18	26.5%
RMSE / MAE	11	16.2%

Table 15: Metric adoption across the 68 surveyed studies. Inference latency is counted when at least one quantitative inference-time indicator is explicitly reported (execution time, frames per second, or end-to-end latency).

Ref.	Year	Sub-system	Platform	Class	Latency (ms)	Power (W)	Accuracy	Memory
[ 48 ]	2025	Track faults (vision)	Zynq 7Z020 / KV260	FPGA	0.7–10	2.3–3.9	93.4%	4.5–11.6 Mb BRAM
[ 87 ]	2020	Traction faults	Xilinx VCU128	FPGA	$<$ 1	NR	$>$ 93%	FPGA resources
[ 82 ]	2024	Track faults (vision)	Xilinx ZCU104	FPGA	2.1	6.9	88.9%	230.5 BRAM
[ 36 ]	2020	Fastener (3D)	Jetson TX2 + Atom	GPU + CPU	$\sim$ 15	NR	99.7%	8 GB
[ 139 ]	2024	In-cabin events	Jetson AGX Xavier	GPU edge	$\sim$ 1200	10–30	85.1%	32 GB
[ 42 ]	2025	Passenger counting	Jetson Nano	GPU edge	80–100	$\sim$ 5–10	96.85%	4 GB

Table 16: Deployment metrics reported across the surveyed studies. Memory values refer to platform RAM capacity (GB) or FPGA on-chip resource usage (BRAM), depending on reporting conventions in each study. NR: not reported.

Platform	Devices	When to Choose	Key Limitation	Railway evidence
CPU/GPU server	Intel Xeon, RTX	Offline training with high compute requirements, no onboard constraints [ 52 , 73 ] .	High power ( $>$ 200 W); non-deterministic latency incompatible with real-time safety-critical systems [ 7 , 34 ] .	Yes (training only)
GPU edge	Jetson TX2, Xavier, Orin	Real-time vision and multimodal tasks under moderate power constraints [ 139 , 36 ] .	Latency variability under concurrent workloads; limited for real-time constraints [ 34 ] .	Yes
FPGA	Xilinx Zynq, ZCU, VCU	Safety-critical subsystems requiring deterministic and low latency [ 87 , 97 ] .	High development complexity; long design cycles; limited flexibility for model updates [ 97 ] .	Yes
Embedded CPU	ARM Cortex-A, Intel Atom	Preprocessing, lightweight inference [ 122 , 149 ] .	Low parallelism; unsuitable for complex deep learning models or high-frequency inference [ 7 ] .	Partial
NPU / AI accel.	Hailo-8, ARM Ethos	Low-power onboard inference with dedicated neural acceleration; strong candidate for future embedded deployment [ 34 ] .	Limited validation in railway applications; integration and certification challenges remain open.	Projected

Table 17: Hardware platforms for deep learning deployment in railway anomaly detection systems. The Railway evidence column indicates whether the platform has been validated in a surveyed railway study (Yes) or is projected from the general embedded AI literature (Projected).

Figure 8: Model compression workflow: quantization, pruning, and knowledge distillation transform a baseline model into a compact version for embedded railway deployment.

Figure 9: Deployment decision framework for deep learning-based anomaly detection in railway systems.

Criterion	Classification	Prediction	Reconstruction	Hybrid
Suitability when fault labels are available	H	M	M	H
Suitability with normal data only	L	H	H	M
Coverage of unseen anomalies	L	M	H	H
Robustness to class imbalance	L	M	H	M
Temporal dependency handling	M	H	M–H	H
Tolerance to operational variability	L–M	M	M	H

Table 18: Analytical comparison of detection paradigms for deployment-oriented railway anomaly detection. H: High suitability; M: Medium; L: Low.

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