Data-Driven Fault Detection in Aircraft Engines With Noisy Sensor Measurements

2011 ◽  
Vol 133 (8) ◽  
Author(s):  
Soumik Sarkar ◽  
Xin Jin ◽  
Asok Ray
Keyword(s):  
Fault Detection ◽  
Time Series Data ◽  
Data Driven ◽  
Sensor Data ◽  
Series Data ◽  
Test Bed ◽  
Symbolic Dynamic Filtering ◽  
Sensor Signals ◽  
Sensor Degradation ◽  
Dynamic Filtering

An inherent difficulty in sensor-data-driven fault detection is that the detection performance could be drastically reduced under sensor degradation (e.g., drift and noise). Complementary to traditional model-based techniques for fault detection, this paper proposes symbolic dynamic filtering by optimally partitioning the time series data of sensor observation. The objective here is to mask the effects of sensor noise level variation and magnify the system fault signatures. In this regard, the concepts of feature extraction and pattern classification are used for fault detection in aircraft gas turbine engines. The proposed methodology of data-driven fault detection is tested and validated on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) test-bed developed by NASA for noisy (i.e., increased variance) sensor signals.

scholarly journals Recent Advances in Anomaly Detection Methods Applied to Aviation

Aerospace ◽  
2019 ◽  
Vol 6 (11) ◽  
pp. 117 ◽  
Author(s):  
Luis Basora ◽  
Xavier Olive ◽  
Thomas Dubot
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Data Driven ◽  
Sensor Data ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Advances ◽  
Flight Operations

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. In particular, we cover unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

scholarly journals Pocketable Labs for Everyone: Synchronized Multi-Sensor Data Streaming and Recording on Smartphones with the Lab Streaming Layer

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8135
Author(s):  
Sarah Blum ◽  
Daniel Hölle ◽  
Martin Georg Bleichner ◽  
Stefan Debener
Keyword(s):  
Data Streams ◽  
Time Series Data ◽  
Sensor Data ◽  
Series Data ◽  
Personal Health ◽  
Data Streaming ◽  
Sensor Signals ◽  
Multiple Sensor ◽  
Android Applications ◽  
Smartphone Sensor

The streaming and recording of smartphone sensor signals is desirable for mHealth, telemedicine, environmental monitoring and other applications. Time series data gathered in these fields typically benefit from the time-synchronized integration of different sensor signals. However, solutions required for this synchronization are mostly available for stationary setups. We hope to contribute to the important emerging field of portable data acquisition by presenting open-source Android applications both for the synchronized streaming (Send-a) and recording (Record-a) of multiple sensor data streams. We validate the applications in terms of functionality, flexibility and precision in fully mobile setups and in hybrid setups combining mobile and desktop hardware. Our results show that the fully mobile solution is equivalent to well-established desktop versions. With the streaming application Send-a and the recording application Record-a, purely smartphone-based setups for mobile research and personal health settings can be realized on off-the-shelf Android devices.

Investigation of Combustion Instability in a Swirl-Stabilized Combustor Using Symbolic Time Series Analysis

2014 ◽  
Author(s):  
Vikram Ramanan ◽  
S. R. Chakravarthy ◽  
Soumalya Sarkar ◽  
Ashok Ray
Keyword(s):  
Time Series ◽  
Fuel Injection ◽  
Time Series Data ◽  
Combustion Instability ◽  
Series Data ◽  
Pressure Amplitude ◽  
Finite State ◽  
Symbolic Dynamic Filtering ◽  
Dynamic Filtering ◽  
The Stability

A laboratory-scale swirl-stabilized combustor is experimentally characterized for various configurations involving variable air flow rates and different fuel injection locations. Unsteady pressure and heat release rate measurements were obtained simultaneously in order to determine the stability map of the combustor for the experimented configurations. It is observed that a sharp rise in pressure amplitude coincides with a break in the dominant spectral content variation with the inlet Reynolds number. The time series data were analyzed by using the tools of symbolic dynamic filtering and the divergences among the outputs of each sub-class of observations were obtained as anomaly measures. In the proposed method, symbol strings are generated by partitioning the (finite-length) time series to construct a special class of probabilistic finite state automata (PFSA) that have a deterministic algebraic structure. The anomaly measures are defined based on the probabilistic state vectors distribution across each sub class. The method which is based on representing a given time series data as a set of PFSA is observed to be capable of predicting an impending combustion instability as well as to distinguish between the symbol-state distribution among various instability conditions. The measure also successfully captures changes in the thermoacoustic regime as a function of the fuel injection location.

scholarly journals Recent Advances in Anomaly Detection Methods applied to Aviation

2019 ◽  
Author(s):  
Luis Basora ◽  
Xavier Olive ◽  
Thomas Dubot
Keyword(s):  
Anomaly Detection ◽  
Time Series Data ◽  
Data Driven ◽  
Sensor Data ◽  
Detection Methods ◽  
Series Data ◽  
Detection Techniques ◽  
Advantages And Disadvantages ◽  
Recent Advances ◽  
Flight Operations

Anomaly detection is an active area of research with numerous methods and applications. This survey reviews the state-of-the-art of data-driven anomaly detection techniques and their application to the the aviation domain. After a brief introduction to the main traditional data-driven methods for anomaly detection, we review the recent advances in the area of neural networks, deep learning and temporal-logic based learning. We cover especially unsupervised techniques applicable to time series data because of their relevance to the aviation domain, where the lack of labeled data is the most usual case, and the nature of flight trajectories and sensor data is sequential, or temporal. The advantages and disadvantages of each method are presented in terms of computational efficiency and detection efficacy. The second part of the survey explores the application of anomaly detection techniques to aviation and their contributions to the improvement of the safety and performance of flight operations and aviation systems. As far as we know, some of the presented methods have not yet found an application in the aviation domain. We review applications ranging from the identification of significant operational events in air traffic operations to the prediction of potential aviation system failures for predictive maintenance.

scholarly journals Elevator Fault Detection Using Profile Extraction and Deep Autoencoder Feature Extraction for Acceleration and Magnetic Signals

2019 ◽  
Vol 9 (15) ◽  
pp. 2990 ◽  
Author(s):  
Krishna Mohan Mishra ◽  
Kalevi Huhtala
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Fault Detection ◽  
Time Series Data ◽  
Data Extraction ◽  
False Positives ◽  
Sensor Data ◽  
Series Data ◽  
False Alarms ◽  
Maintenance Systems

In this paper, we propose a new algorithm for data extraction from time-series data, and furthermore automatic calculation of highly informative deep features to be used in fault detection. In data extraction, elevator start and stop events are extracted from sensor data including both acceleration and magnetic signals. In addition, a generic deep autoencoder model is also developed for automated feature extraction from the extracted profiles. After this, extracted deep features are classified with random forest algorithm for fault detection. Sensor data are labelled as healthy and faulty based on the maintenance actions recorded. The remaining healthy data are used for validation of the model to prove its efficacy in terms of avoiding false positives. We have achieved above 90% accuracy in fault detection along with avoiding false positives based on new extracted deep features, which outperforms results using existing features. Existing features are also classified with random forest to compare results. Our developed algorithm provides better results due to the new deep features extracted from the dataset when compared to existing features. This research will help various predictive maintenance systems to detect false alarms, which will in turn reduce unnecessary visits of service technicians to installation sites.

scholarly journals Towards Providing Effective Data-Driven Responses to Predict the Covid-19 in São Paulo and Brazil

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 540
Author(s):  
Fabio Amaral ◽  
Wallace Casaca ◽  
Cassio M. Oishi ◽  
José A. Cuminato
Keyword(s):  
Learning Strategies ◽  
Time Series Data ◽  
Sao Paulo ◽  
The State ◽  
Data Driven ◽  
Series Data ◽  
São Paulo ◽  
Time Data ◽  
Unified Framework ◽  
Infected People

São Paulo is the most populous state in Brazil, home to around 22% of the country’s population. The total number of Covid-19-infected people in São Paulo has reached more than 1 million, while its total death toll stands at 25% of all the country’s fatalities. Joining the Brazilian academia efforts in the fight against Covid-19, in this paper we describe a unified framework for monitoring and forecasting the Covid-19 progress in the state of São Paulo. More specifically, a freely available, online platform to collect and exploit Covid-19 time-series data is presented, supporting decision-makers while still allowing the general public to interact with data from different regions of the state. Moreover, a novel forecasting data-driven method has also been proposed, by combining the so-called Susceptible-Infectious-Recovered-Deceased model with machine learning strategies to better fit the mathematical model’s coefficients for predicting Infections, Recoveries, Deaths, and Viral Reproduction Numbers. We show that the obtained predictor is capable of dealing with badly conditioned data samples while still delivering accurate 10-day predictions. Our integrated computational system can be used for guiding government actions mainly in two basic aspects: real-time data assessment and dynamic predictions of Covid-19 curves for different regions of the state. We extend our analysis and investigation to inspect the virus spreading in Brazil in its regions. Finally, experiments involving the Covid-19 advance in other countries are also given.

scholarly journals Remaining Useful Life Prediction Using Temporal Convolution with Attention

AI ◽  
2021 ◽  
Vol 2 (1) ◽  
pp. 48-70
Author(s):  
Wei Ming Tan ◽  
T. Hui Teo
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Time Series Data ◽  
Remaining Useful Life ◽  
Sensor Data ◽  
Series Data ◽  
Multiple Time ◽  
Data Set ◽  
Form Complex ◽  
Useful Life

Prognostic techniques attempt to predict the Remaining Useful Life (RUL) of a subsystem or a component. Such techniques often use sensor data which are periodically measured and recorded into a time series data set. Such multivariate data sets form complex and non-linear inter-dependencies through recorded time steps and between sensors. Many current existing algorithms for prognostic purposes starts to explore Deep Neural Network (DNN) and its effectiveness in the field. Although Deep Learning (DL) techniques outperform the traditional prognostic algorithms, the networks are generally complex to deploy or train. This paper proposes a Multi-variable Time Series (MTS) focused approach to prognostics that implements a lightweight Convolutional Neural Network (CNN) with attention mechanism. The convolution filters work to extract the abstract temporal patterns from the multiple time series, while the attention mechanisms review the information across the time axis and select the relevant information. The results suggest that the proposed method not only produces a superior accuracy of RUL estimation but it also trains many folds faster than the reported works. The superiority of deploying the network is also demonstrated on a lightweight hardware platform by not just being much compact, but also more efficient for the resource restricted environment.

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