Avionic Main Fuel Pump Simulation and Fault-Diagnosis Benchmark
- URL: http://arxiv.org/abs/2604.22869v1
- Date: Thu, 23 Apr 2026 19:08:54 GMT
- Title: Avionic Main Fuel Pump Simulation and Fault-Diagnosis Benchmark
- Abstract summary: In many cyber-physical systems, especially in critical applications such as aeroplanes, data to train anomaly detection and diagnosis algorithms is lacking due to data protection issues and partial observability.<n>We introduce a high-fidelity, physics-informed co-simulation of a common aircraft main-fuel-pump system modelled in textscMATLAB/Simulink Simscape Fluids.
- Score: 40.16504335998919
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: In many cyber-physical systems, especially in critical applications such as aeroplanes, data to train anomaly detection and diagnosis algorithms is lacking due to data protection issues and partial observability. To combat this inherent lack of data, we introduce a high-fidelity, physics-informed co-simulation of a common aircraft main-fuel-pump system modelled in \textsc{MATLAB/Simulink Simscape Fluids}. We also describe its generated time-series data with health and fault mode annotations. To show feasibility of our benchmark, we apply an unsupervised Recurrent Variational Autoencoder (RNN-VAE) for anomaly detection and a SOM-VAE for operating mode discretization, trained to separate healthy and faulty conditions.
Related papers
- An Open-Access Benchmark of Statistical and Machine-Learning Anomaly Detection Methods for Battery Applications [0.0]
OSBAD is an open-source benchmark for anomaly detection frameworks in battery applications.<n>By benchmarking 15 diverse algorithms, OSBAD enables a systematic comparison of anomaly detection methods.
arXiv Detail & Related papers (2025-11-03T16:57:18Z) - Causal Disentanglement Hidden Markov Model for Fault Diagnosis [55.90917958154425]
We propose a Causal Disentanglement Hidden Markov model (CDHM) to learn the causality in the bearing fault mechanism.
Specifically, we make full use of the time-series data and progressively disentangle the vibration signal into fault-relevant and fault-irrelevant factors.
To expand the scope of the application, we adopt unsupervised domain adaptation to transfer the learned disentangled representations to other working environments.
arXiv Detail & Related papers (2023-08-06T05:58:45Z) - Machine Learning for UAV Propeller Fault Detection based on a Hybrid
Data Generation Model [0.0]
This paper focuses on the identification of faulty propellers and classification of the fault level for quadrotor UAVs using RPM as well as flight data.
To achieve offline training data generation, a hybrid approach is proposed for the development of a virtual data-generative model.
The experimental results obtained show that our trained model can identify the location of propeller fault as well as the degree/type of damage.
arXiv Detail & Related papers (2023-02-03T05:28:02Z) - An Outlier Exposure Approach to Improve Visual Anomaly Detection
Performance for Mobile Robots [76.36017224414523]
We consider the problem of building visual anomaly detection systems for mobile robots.
Standard anomaly detection models are trained using large datasets composed only of non-anomalous data.
We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
arXiv Detail & Related papers (2022-09-20T15:18:13Z) - In-flight Novelty Detection with Convolutional Neural Networks [0.0]
This paper proposes that system output measurements are prioritised in real-time for the attention of preventative maintenance decision makers.
We present a data-driven system for online detection and prioritisation of anomalous data.
The system is capable of running in real-time on low-power embedded hardware and is currently in deployment on the Rolls-Royce Pearl 15 engine flight trials.
arXiv Detail & Related papers (2021-12-07T15:19:41Z) - Cloud Failure Prediction with Hierarchical Temporary Memory: An
Empirical Assessment [64.73243241568555]
Hierarchical Temporary Memory (HTM) is an unsupervised learning algorithm inspired by the features of the neocortex.
This paper presents the first systematic study that assesses HTM in the context of failure prediction.
arXiv Detail & Related papers (2021-10-06T07:09:45Z) - Data-driven Residual Generation for Early Fault Detection with Limited
Data [4.129225533930966]
In many complex systems it is not feasible to develop highly accurate models for the systems.
Data-driven solutions have received an immense attention in the industries systems for several practical reasons.
Unlike the model-based methods it is straight forward to combine time series measurements such as pressure and voltage with other sources of information.
arXiv Detail & Related papers (2021-09-28T03:18:03Z) - DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly
detection in air transportation [68.8204255655161]
We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE)
It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase.
Results show that the DAE achieves better results in both accuracy and speed of detection.
arXiv Detail & Related papers (2021-09-08T14:07:55Z) - TadGAN: Time Series Anomaly Detection Using Generative Adversarial
Networks [73.01104041298031]
TadGAN is an unsupervised anomaly detection approach built on Generative Adversarial Networks (GANs)
To capture the temporal correlations of time series, we use LSTM Recurrent Neural Networks as base models for Generators and Critics.
To demonstrate the performance and generalizability of our approach, we test several anomaly scoring techniques and report the best-suited one.
arXiv Detail & Related papers (2020-09-16T15:52:04Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.