scholarly journals Improving Spacecraft Health Monitoring with Automatic Anomaly Detection Techniques

2016 ◽  
Author(s):  
Sylvain Fuertes ◽  
Gilles Picart ◽  
Jean-Yves Tourneret ◽  
Lotfi Chaari ◽  
André Ferrari ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Health Monitoring ◽  
Detection Techniques

scholarly journals Aircraft Fleet Health Monitoring with Anomaly Detection Techniques

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 103
Author(s):  
Luis Basora ◽  
Paloma Bry ◽  
Xavier Olive ◽  
Floris Freeman
Keyword(s):  
Anomaly Detection ◽  
Health Monitoring ◽  
Health Management ◽  
Sensor Data ◽  
Detection Methods ◽  
Aviation Industry ◽  
Nominal Data ◽  
Detection Techniques ◽  
Aircraft System ◽  
Anomaly Score

Predictive maintenance has received considerable attention in the aviation industry where costs, system availability and reliability are major concerns. In spite of recent advances, effective health monitoring and prognostics for the scheduling of condition-based maintenance operations is still very challenging. The increasing availability of maintenance and operational data along with recent progress made in machine learning has boosted the development of data-driven prognostics and health management (PHM) models. In this paper, we describe the data workflow in place at an airline for the maintenance of an aircraft system and highlight the difficulties related to a proper labelling of the health status of such systems, resulting in a poor suitability of supervised learning techniques. We focus on investigating the feasibility and the potential of semi-supervised anomaly detection methods for the health monitoring of a real aircraft system. Proposed methods are evaluated on large volumes of real sensor data from a cooling unit system on a modern wide body aircraft from a major European airline. For the sake of confidentiality, data has been anonymized and only few technical and operational details about the system had been made available. We trained several deep neural network autoencoder architectures on nominal data and used the anomaly scores to calculate a health indicator. Results suggest that high anomaly scores are correlated with identified failures in the maintenance logs. Also, some situations see an increase in the anomaly score for several flights prior to the system’s failure, which paves a natural way for early fault identification.

scholarly journals Indirectly Supervised Anomaly Detection of Clinically Meaningful Health Events from Smart Home Data

2021 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Jessamyn Dahmen ◽  
Diane J. Cook
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Bayesian Optimization ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Health Events ◽  
Warm Start ◽  
Health Related ◽  
Unsupervised Algorithms

Anomaly detection techniques can extract a wealth of information about unusual events. Unfortunately, these methods yield an abundance of findings that are not of interest, obscuring relevant anomalies. In this work, we improve upon traditional anomaly detection methods by introducing Isudra, an Indirectly Supervised Detector of Relevant Anomalies from time series data. Isudra employs Bayesian optimization to select time scales, features, base detector algorithms, and algorithm hyperparameters that increase true positive and decrease false positive detection. This optimization is driven by a small amount of example anomalies, driving an indirectly supervised approach to anomaly detection. Additionally, we enhance the approach by introducing a warm-start method that reduces optimization time between similar problems. We validate the feasibility of Isudra to detect clinically relevant behavior anomalies from over 2M sensor readings collected in five smart homes, reflecting 26 health events. Results indicate that indirectly supervised anomaly detection outperforms both supervised and unsupervised algorithms at detecting instances of health-related anomalies such as falls, nocturia, depression, and weakness.

One-Class-Based Intelligent Classifier for Detecting Anomalous Situations During the Anesthetic Process

2020 ◽  
Author(s):  
Alberto Leira ◽  
Esteban Jove ◽  
Jose M Gonzalez-Cava ◽  
José-Luis Casteleiro-Roca ◽  
Héctor Quintián ◽  
...  
Keyword(s):  
Side Effects ◽  
Anomaly Detection ◽  
General Anesthesia ◽  
Closed Loop ◽  
Real Data ◽  
Successful Performance ◽  
Final Model ◽  
Detection Techniques ◽  
Adverse Side Effects ◽  
Operative Recovery

Abstract Closed-loop administration of propofol for the control of hypnosis in anesthesia has evidenced an outperformance when comparing it with manual administration in terms of drug consumption and post-operative recovery of patients. Unlike other systems, the success of this strategy lies on the availability of a feedback variable capable of quantifying the current hypnotic state of the patient. However, the appearance of anomalies during the anesthetic process may result in inaccurate actions of the automatic controller. These anomalies may come from the monitors, the syringe pumps, the actions of the surgeon or even from alterations in patients. This could produce adverse side effects that can affect the patient postoperative and reduce the safety of the patient in the operating room. Then, the use of anomaly detection techniques plays a significant role to avoid this undesirable situations. This work assesses different one-class intelligent techniques to detect anomalies in patients undergoing general anesthesia. Due to the difficulty of obtaining real data from anomaly situations, artificial outliers are generated to check the performance of each classifier. The final model presents successful performance.

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

Efficient Implementation of Hyperspectral Anomaly Detection Techniques on GPUs and Multicore Processors

2014 ◽  
Vol 7 (6) ◽  
pp. 2256-2266 ◽  
Author(s):  
Jose M. Molero ◽  
Ester M. Garzon ◽  
Inmaculada Garcia ◽  
Enrique S. Quintana-Orti ◽  
Antonio Plaza
Keyword(s):  
Anomaly Detection ◽  
Multicore Processors ◽  
Efficient Implementation ◽  
Detection Techniques
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