Understanding the Impact of Statistical Time Series Features for Flare Prediction Analysis

Hostetter, Maxwell; Ahmadzadeh, Azim; Aydin, Berkay; Georgoulis, Manolis K.; Kempton, Dustin J.; Angryk, Rafal A.

doi:10.1109/bigdata47090.2019.9006116

Cited by 10 publications

(6 citation statements)

References 19 publications

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“…One such data driven solution approach is machine learning (ML), where patterns in large data sets are brought to the surface by finding complex mathematical relationships within the data [47]. Researchers are therefore gradually exploring multivariate data analysis techniques from the field of ML in order to approximate future occurrences of space weather events from past distribution patterns [48]. Current studies have clarified patterns of spatial sensitivity, however temporal forecasts have remained largely empirical [49], [50].…”

Section: Motivationmentioning

confidence: 99%

Using Support Vector Machine (SVM) and Ionospheric Total Electron Content (TEC) Data for Solar Flare Predictions

Asaly

Gottlieb

Reuveni

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Section: Motivationmentioning

confidence: 99%

Using Support Vector Machine (SVM) and Ionospheric Total Electron Content (TEC) Data for Solar Flare Predictions

Asaly

Gottlieb

Reuveni

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…To forecast a time series using these machine learning models, it is necessary to capture the temporal order information properly as new attributes in each observation itself. There are two general approaches [14]. The first approach simply appends all the information from the considered previous time segments into the observation [15].…”

Section: Data Preprocessingmentioning

confidence: 99%

“…The time series may also be normalized [16], stationarized [17], or decomposed [18] a priori. The second approach only supplements the observation with specifically engineered and selected statistical features from the time series for the learning [14]. The first approach will be adopted here due to its simplicity.…”

Section: Data Preprocessingmentioning

confidence: 99%

Ensemble Methods for APS In-Flight Particle Temperature and Velocity Prediction Considering Torch Electrodes Ageing

Cojocaru

Ilinca

et al. 2021

Thermal Spray 2021: Proceedings From the International Thermal Spray Conference

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In an atmospheric plasma spray (APS) process; in-flight powder particle characteristics; such as the particle velocity and temperature; have significant influence on the coating formation. The nonlinear relationship between the input process parameters and in-flight particle characteristics is thus of paramount importance for coating properties design and quality control. It is also known that the ageing of torch electrodes affects this relationship. In recent years; machine learning algorithms have proven to be able to take into account such complex nonlinear interactions. This work illustrates the application of ensemble methods based on decision tree algorithms to evaluate and to predict in-flight particle temperature and velocity during an APS process considering torch electrodes ageing. Experiments were performed to record simultaneously the input process parameters; the in-flight powder particle characteristics and the electrodes usage time. Various spray durations were considered to emulate industrial coating spray production settings. Random forest and gradient boosting algorithms were used to rank and select the features for the APS process data recorded as the electrodes aged and the corresponding predictive models were compared. The time series aspect of the data will be examined.

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“…On the other hand, many other works have been focused on the use of statistical time features (STFs) and the proper selection of such input features. Consequently, efficient results have been obtained on applications related to rotatory machinery [ 27 , 28 , 29 ], sensor fault detection [ 30 ], civil structures [ 10 ], solar flare prediction [ 31 ], cardiac diseases [ 32 ], epileptic seizure detection [ 33 ], amputees limb motion [ 34 ], and neurodegenerative diseases [ 35 ], among others. In general, STFs are used to find and describe relevant signal properties and, at the same time, differentiate between different types of signals or classes, e.g., a healthy condition class and a fault condition class.…”

Section: Introductionmentioning

confidence: 99%

Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA

Huerta-Rosales

Granados-Lieberman

García-Pérez

et al. 2021

Sensors

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One of the most critical devices in an electrical system is the transformer. It is continuously under different electrical and mechanical stresses that can produce failures in its components and other electrical network devices. The short-circuited turns (SCTs) are a common winding failure. This type of fault has been widely studied in literature employing the vibration signals produced in the transformer. Although promising results have been obtained, it is not a trivial task if different severity levels and a common high-level noise are considered. This paper presents a methodology based on statistical time features (STFs) and support vector machines (SVM) to diagnose a transformer under several SCTs conditions. As STFs, 19 indicators from the transformer vibration signals are computed; then, the most discriminant features are selected using the Fisher score analysis, and the linear discriminant analysis is used for dimension reduction. Finally, a support vector machine classifier is employed to carry out the diagnosis in an automatic way. Once the methodology has been developed, it is implemented on a field-programmable gate array (FPGA) to provide a system-on-a-chip solution. A modified transformer capable of emulating different SCTs severities is employed to validate and test the methodology and its FPGA implementation. Results demonstrate the effectiveness of the proposal for diagnosing the transformer condition as an accuracy of 96.82% is obtained.

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Understanding the Impact of Statistical Time Series Features for Flare Prediction Analysis

Cited by 10 publications

References 19 publications

Using Support Vector Machine (SVM) and Ionospheric Total Electron Content (TEC) Data for Solar Flare Predictions

Using Support Vector Machine (SVM) and Ionospheric Total Electron Content (TEC) Data for Solar Flare Predictions

Ensemble Methods for APS In-Flight Particle Temperature and Velocity Prediction Considering Torch Electrodes Ageing

Short-Circuited Turn Fault Diagnosis in Transformers by Using Vibration Signals, Statistical Time Features, and Support Vector Machines on FPGA

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