Enhanced Telemetry Monitoring with Novelty Detection

Martínez-Heras, José-Antonio; Donati, Alessandro

doi:10.1609/aimag.v35i4.2553

Cited by 30 publications

(7 citation statements)

References 12 publications

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“…When new telemetry data arrives, its distance to the nearest nominal region is measured, which provides a measure of a point anomaly relative to the well-defined clusters [28,32]. Several evolutions of it have been created such as the Anomaly Monitoring Inductive Software System (AMISS) and Ames to [2] and also on board the Space Shuttle and the International Space Station [26], as well as the XMM-Newton satellite [33]. Automated Telemetry Health Monitoring System (ATHMoS), developed by the German Aerospace Center, used concepts behind the LoOP and Intrinsic Dimension Outlier Score (IDOS) outlier detection algorithms to create the Outlier Probability Via Intrinsic Dimension (OPVID) algorithm.…”

Section: Anomaly Detectionmentioning

confidence: 99%

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

Wang

Gong

Zhang

et al. 2022

International Journal of Aerospace Engineering

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Reducing satellite failures and keeping satellites healthy in orbit are important issues. Current satellite systems have developed modules to detect anomalies on board. However, they only target a subset of anomaly types and heavily rely on expert knowledge. To address these limitations, this paper proposes a data-driven anomaly detection framework to detect point anomalies. We first propose the Deviation Divide Mean over Neighbors (DDMN) method to figure out the fake anomaly problem caused by data errors in the satellite telemetry data. Then, we use the Long Short-Term Memory (LSTM), a deep learning method, to model the multivariable time-series data, and a Gaussian model to detect anomalies. We applied our approach to the telemetry data collected from sensors on an in-orbit satellite for more than two years and demonstrate its superiority. Moreover, we explored what conditions could lead to false alarms. The approach proposed has been deployed to the ground station to monitor the health status of the in-orbit satellites.

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Section: Anomaly Detectionmentioning

confidence: 99%

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

Wang

Gong

Zhang

et al. 2022

International Journal of Aerospace Engineering

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“…Many algorithms have been proposed to address time series anomaly detection [12,22]. The most basic approaches simply flag regions where values exceed a certain threshold [19,35]. More advanced methods are based on statistical hypothesis testing [51], clustering [25,26], and/or machine learning [47].…”

Section: Related Workmentioning

confidence: 99%

Sintel: A Machine Learning Framework to Extract Insights from Signals

Alnegheimish,

Liu,

Sala

et al. 2022

Preprint

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The detection of anomalies in time series data is a critical task with many monitoring applications. Existing systems often fail to encompass an end-to-end detection process, to facilitate comparative analysis of various anomaly detection methods, or to incorporate human knowledge to refine output. This precludes current methods from being used in real-world settings by practitioners who are not ML experts. In this paper, we introduce Sintel, a machine learning framework for end-to-end time series tasks such as anomaly detection. The framework uses state-of-the-art approaches to support all steps of the anomaly detection process. Sintel logs the entire anomaly detection journey, providing detailed documentation of anomalies over time. It enables users to analyze signals, compare methods, and investigate anomalies through an interactive visualization tool, where they can annotate, modify, create, and remove events. Using these annotations, the framework leverages human knowledge to improve the anomaly detection pipeline. We demonstrate the usability, efficiency, and effectiveness of Sintel through a series of experiments on three public time series datasets, as well as one real-world use case involving spacecraft experts tasked with anomaly analysis tasks. Sintel's framework, code, and datasets are open-sourced at https://github.com/sintel-dev/.

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“…In general, supervised, semi-supervised, or unsupervised approaches can be used for detecting anomalies based on the availability of the labeled data. Supervised learning approaches [14,15] need labeled dataset for training model and can only detect known anomalies [16]. However, domain experts are required for the labeling process since most datasets for anomaly detection are not labeled.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder

et al. 2021

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Detecting anomalies in the Brake Operating Unit (BOU) braking system of metro trains is very important for trains’ reliability and safety. However, current periodic maintenance and inspection cannot detect anomalies at an early stage. In addition, constructing a stable and accurate anomaly detection system is a very challenging task. Hence, in this work, we propose a method for detecting anomalies of BOU on metro vehicles using a one-class long short-term memory (LSTM) autoencoder. First, we extracted brake cylinder (BC) pressure data from the BOU data since one of the anomaly cases of metro trains is that BC pressure relief time is delayed by 4 s. After that, extracted BC pressure data is split into subsequences which are fed into our proposed one-class LSTM autoencoder which consists of two LSTM blocks (encoder and decoder). The one-class LSTM autoencoder is trained using training data which only consists of normal subsequences. To detect anomalies from test data that contain abnormal subsequences, the mean absolute error (MAE) for each subsequence is calculated. When the error is larger than a predefined threshold which was set to the maximum value of MAE in the training (normal) dataset, we can declare that example an anomaly. We conducted the experiments with the BOU data of metro trains in Korea. Experimental results show that our proposed method can detect anomalies of the BOU data well.

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Enhanced Telemetry Monitoring with Novelty Detection

Cited by 30 publications

References 12 publications

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

A Deep Learning Anomaly Detection Framework for Satellite Telemetry with Fake Anomalies

Sintel: A Machine Learning Framework to Extract Insights from Signals

Anomaly Detection of the Brake Operating Unit on Metro Vehicles Using a One-Class LSTM Autoencoder

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