Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Gao, Yuan; Cheong, Benjamin; Bozhko, Serhiy; Wheeler, Pat; Gerada, Chris; Yang, Tao

doi:10.1016/j.cja.2022.08.011

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…The solution to this problem was implemented in work [9], by recognizing a fragment of one of the given sets of reference signals, which is included in the analyzed spectrum at the current time. The main advantage of this method is efficiency and the ability to work with signals for which it is impossible to calculate the first derivative.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Designing tools for assessing the reliability of electric motor torque measurements by using identifiers of anomalous deviations in a noisy signal system

Kvasnikov,

Kvashuk,

Prygara

et al. 2023

EEJET

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The problem of the reliability of measurements of rotational parameters of electric motors was solved, which was focused on the development of an algorithm for evaluating measurements under conditions of additional noise. An analysis of methodological approaches and mathematical tools used to process and interpret the uncertainty of measurement results was carried out. Cases where they may not be effective due to high noise levels were considered. To detect anomalies in the signal, an algorithm for assessing the reliability of measurements using fuzzy logic was proposed. A structural diagram of the model for measuring the torque of an electric motor under the conditions of a noisy signal was developed, where transfer functions were used to model the angular velocity and torque parameters. A method for detecting anomalies in noisy signals is presented, which identifies the amplitude and time characteristics of spiking pulses. The method includes the application of a wide range of analytical tools for deep analysis of signals and is particularly effective for detecting anomalies that may be hidden in background noise. A prototype of a measuring bench was developed, which uses neural networks to detect anomalies when measuring the rotational parameters of electric motors, which made it possible to obtain a training sample using a sample electric motor and apply it to evaluate the parameters of another electric motor. In a practical aspect, the developed methods and technological solutions for improving the reliability of measurements of rotational parameters of electric motors could be used to make corrections in existing systems. In particular, they could be used in industry, electric transport, as well as in the aerospace and military sectors where the reliability of measuring systems is important

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Designing tools for assessing the reliability of electric motor torque measurements by using identifiers of anomalous deviations in a noisy signal system

Kvasnikov,

Kvashuk,

Prygara

et al. 2023

EEJET

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“…Modern review articles [15,16] that cover the existing methods of AC drive diagnostics reveal several common trends in the development of diagnostics systems. These include the search for new means of measuring parameters [17,18], the search for algorithms for determining specific types of defects in electrical equipment [19,20], system approaches in solving diagnostic problems [21,22], and the use of artificial intelligence in diagnostic problems [23,24]. As we can conclude from the observed scientific papers, the given ways and means of diagnostics require additional investments in sensors, specialized devices and equipment, highly competent employees, system architecture, and others.…”

Section: Introductionmentioning

confidence: 99%

A Diagnostic Curve for Online Fault Detection in AC Drives

Koteleva,

Korolev

2024

Energies

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The AC drive is an important component and the most common element of any manufacturing process. A particularly serious task is the proper assessment of the AC drive’s technical condition, as its failure can cause problems for entire units and complexes of industrial enterprises. At present, there are several approaches either to determine electric drives’ condition or to find certain defects. Frequently, these methods require the installation of additional equipment that exceeds the price of the electric drive by several times. In this work, a simple approach is proposed. It includes the use of a diagnostic curve to assess the condition. This diagnostic curve is produced from the measurement results of the current sensors on the drive. Based on the Park vector modification, this is a simple and affordable way to obtain real-time information. The obtained curve can be used for the following purposes: directly for condition assessment by visual monitoring, as a sign for diagnostic systems built on artificial intelligence methods, for dynamic tuning of the drive control system. The article gives the algorithm for obtaining the diagnostic curve, showing its efficiency for model and field experiments. In model experiments, the faults in the rotor and stator of the drive were simulated; in field experiments, the state was analyzed by changing the load on the motor.

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Time-history performance optimization of flapping wing motion using a deep learning based prediction model

WANG,

LIU,

et al. 2024

Chinese Journal of Aeronautics

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Surrogate role of machine learning in motor-drive optimization for more-electric aircraft applications

Cited by 6 publications

References 34 publications

Designing tools for assessing the reliability of electric motor torque measurements by using identifiers of anomalous deviations in a noisy signal system

Designing tools for assessing the reliability of electric motor torque measurements by using identifiers of anomalous deviations in a noisy signal system

A Diagnostic Curve for Online Fault Detection in AC Drives

Time-history performance optimization of flapping wing motion using a deep learning based prediction model

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