Anomaly Detection for Electro-Hydrostatic Actuators using LSTM Autoencoder

• URL: http://arxiv.org/abs/2606.05274v1
• Date: Wed, 03 Jun 2026 17:37:21 GMT
• Title: Anomaly Detection for Electro-Hydrostatic Actuators using LSTM Autoencoder
• Abstract summary: This study presents an offline anomaly-detection framework for EHA sensor signals, focusing on temperature and pressure data collected from a controlled test bench.<n>The method employs a reconstruction-based Long Short-Term Memory (LSTM) autoencoder, calibrated and evaluated using validation-set reconstruction-error distributions.<n>The LSTM autoencoder achieved an average accuracy of 99.0%, precision up to 100%, recall between 90.2% and 99.6%, and F1-scores from 93.1% to 99.8%, demonstrating high detection sensitivity and a very low false-
• Score: 0.3262230127283452
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Electro-Hydrostatic Actuators (EHAs) are widely used in aerospace and industrial systems, where timely detection of sensor anomalies is essential to ensure safe and reliable operation. However, the large volume and high sampling frequency of EHA sensor data pose challenges for accurate and efficient anomaly detection. Conventional statistical and classical machine-learning methods such as Z-score, Interquartile Range (IQR), Median Absolute Deviation (MAD), Isolation Forest, Gaussian Mixture, and k-means often fail to capture the temporal dependencies inherent in EHA signals, resulting in limited detection accuracy and elevated false-alarm rates. Furthermore, systematic evaluations of data-driven anomaly detection approaches for EHA systems remain scarce, particularly under varying operational conditions. This study presents an offline anomaly-detection framework for univariate EHA sensor signals, focusing on temperature and pressure data collected from a controlled test bench. The method employs a reconstruction-based Long Short-Term Memory (LSTM) autoencoder, calibrated and evaluated using validation-set reconstruction-error distributions. Performance is assessed across multiple fault-injection scenarios using accuracy, precision, recall, and F1-score, complemented by sensitivity analyses under varying operating conditions. The LSTM autoencoder achieved an average accuracy of 99.0\%, precision up to 100\%, recall between 90.2\% and 99.6\%, and F1-scores from 93.1\% to 99.8\%, demonstrating high detection sensitivity and a very low false-alarm rate across all evaluated sensors. These results highlight the feasibility of data-driven offline anomaly detection for EHAs. Future work will focus on adapting the developed framework for an online (real-time) environment.

Related papers

• Demographic-Aware Self-Supervised Anomaly Detection Pretraining for Equitable Rare Cardiac Diagnosis [45.00216485193071]
  Rare cardiac anomalies are difficult to detect from electrocardiograms due to their long-tailed distribution.<n>We developed an AI-assisted two-stage ECG framework integrating self-supervised anomaly detection and demographic-aware representation learning.<n>Our method achieves an AUROC of 94.7% for rare anomalies and reduces the common-rare performance gap by 73%.
  arXiv  Detail & Related papers  (2026-03-20T07:04:00Z)
• Calibration-Free EEG-based Driver Drowsiness Detection with Online Test-Time Adaptation [43.83587667787693]
  Drowsy driving is a growing cause of traffic accidents, prompting recent exploration of electroencephalography (EEG)-based drowsiness detection systems.<n>In particular, the inter-subject variability of EEG signals leads to a domain shift problem, which makes it challenging to generalize drowsiness detection models to unseen target subjects.<n>We propose a novel driver drowsiness detection framework that leverages online test-time adaptation methods to dynamically adjust to target subject distributions.
  arXiv  Detail & Related papers  (2025-11-27T02:27:10Z)
• An Efficient Anomaly Detection Framework for Wireless Sensor Networks Using Markov Process [2.5777932046298786]
  A lightweight and interpretable anomaly detection framework based on a first order Markov chain model has been proposed.<n>The proposed framework was validated using the Intel Berkeley Research Lab dataset.
  arXiv  Detail & Related papers  (2025-11-01T10:19:00Z)
• GRAD: Real-Time Gated Recurrent Anomaly Detection in Autonomous Vehicle Sensors Using Reinforced EMA and Multi-Stage Sliding Window Techniques [0.0]
  This paper introduces GRAD, a real-time anomaly detection method for autonomous vehicle sensors.<n>The proposed approach combines the Reinforced Exponential Moving Average (REMA), which adapts smoothing factors and thresholding for outlier detection, with the Multi-Stage Sliding Window (MS-SW) technique for capturing both short- and long-term patterns.<n> GRAD has a lightweight architecture consisting of two layers of GRU with a limited number of neurons that make it appropriate for real-time applications.
  arXiv  Detail & Related papers  (2025-10-27T13:44:15Z)
• Robust Anomaly Detection in Industrial Environments via Meta-Learning [12.690752260618522]
  This paper presents RAD, a robust anomaly detection framework that integrates Normalizing Flows with Model-Agnostic Meta-Learning.<n>Our approach employs a bi-level optimization strategy where meta-learning enables rapid adaptation to varying noise conditions.<n>Our results highlight RAD's exceptional resilience to noisy training conditions and its ability to detect subtle anomalies across diverse industrial scenarios.
  arXiv  Detail & Related papers  (2025-08-25T08:35:28Z)
• AI-Powered Machine Learning Approaches for Fault Diagnosis in Industrial Pumps [0.0]
  This study presents a practical approach for early fault detection in industrial pump systems using real-world sensor data.<n>The framework is scalable, interpretable, and suitable for real-time industrial deployment.
  arXiv  Detail & Related papers  (2025-08-21T13:33:09Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• Accelerating Cavity Fault Prediction Using Deep Learning at Jefferson Laboratory [0.4218593777811082]
  Accelerating cavities are an integral part of the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory. In this study, we propose the use of a deep learning model to predict slowly developing cavity faults.
  arXiv  Detail & Related papers  (2024-04-24T12:05:20Z)
• PULL: Reactive Log Anomaly Detection Based On Iterative PU Learning [58.85063149619348]
  We propose PULL, an iterative log analysis method for reactive anomaly detection based on estimated failure time windows. Our evaluation shows that PULL consistently outperforms ten benchmark baselines across three different datasets.
  arXiv  Detail & Related papers  (2023-01-25T16:34:43Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Bayesian Autoencoders for Drift Detection in Industrial Environments [69.93875748095574]
  Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. Anomalies can come either from real changes in the environment (real drift) or from faulty sensory devices (virtual drift)
  arXiv  Detail & Related papers  (2021-07-28T10:19:58Z)

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.