Time–Frequency Analysis of Experimental Measurements for the Determination of EMI Noise Generators in Power Converters

Goyalde, Javier Oyarzun; Aizpuru, Iosu; Baraia-Etxaburu, Igor

doi:10.3390/electronics11233898

Cited by 3 publications

(1 citation statement)

References 54 publications

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“…The states of healthy and damaged blade of a quadcopter are studied based on signal processing in real-time decisionmaking process [42]. However, sophisticated techniques such as Continuous Wavelet Transform (CWT) [43,44] and Discrete Wavelet Transform (DWT) [45][46][47][48]…”

Section: Fault Diagnosis Strategies For Predictive Maintenance Ismentioning

confidence: 99%

Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?

Al-Haddad,

Giernacki,

Shandookh

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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In the modern technological advancements, Unmanned Aerial Vehicles (UAVs) have emerged across diverse applications. As UAVs evolve, fault diagnosis witnessed great advancements, with signal processing methodologies taking center stage. This paper presents an assessment of vibration-based signal processing techniques, focusing on Kalman filtering (KF) and Discrete Wavelet Transform (DWT) multiresolution analysis. Experimental evaluation of healthy and faulty states in a quadcopter, using an accelerometer, are presented. The determination of the 1024 Hz sampling frequency is facilitated through finite element analysis of 20 mode shapes. KF exhibits commendable performance, successfully segregating faulty and healthy peaks within an acceptable range. While the six-level multi-decomposition unveils good explanations for fluctuations eluding KF. Ultimately, both KF and DWT showcase high-performance capabilities in fault diagnosis. However, DWT shows superior assessment precision, uncovering intricate details and facilitating a holistic understanding of fault-related characteristics.

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Section: Fault Diagnosis Strategies For Predictive Maintenance Ismentioning

confidence: 99%

Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?

Al-Haddad,

Giernacki,

Shandookh

et al. 2023

Eksploatacja i Niezawodność – Maintenance and Reliability

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Fault detection method of new energy vehicle engine based on wavelet transform and support vector machine

Yuan,

Yang

2024

International Journal of Knowledge-Based and Intelligent Engine

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In order to solve the problem that existing methods are affected by fundamental frequency and harmonic frequency oscillation of new energy vehicle engine, a new energy vehicle engine fault detection method based on wavelet transform and support vector machine is proposed. Firstly, a detection model of abnormal noise signal of automobile engine fault is established, and the time-frequency parameters of basis function are adjusted adaptively. Then, the mechanical excitation component and the battery excitation component in the engine surface radiation noise are separated, and the new energy vehicle engine fault signal is decomposed by feature decomposition and multi-scale separation. Finally, wavelet transform combined with support vector machine algorithm was used to extract fault features of new energy vehicles, fuzzy clustering was carried out, and time-frequency analysis of fault signals was carried out in fractional Fourier domain to realize fault detection of new energy vehicle engines. The test results show that the method has high fault identification level and good clustering of fault feature points for new energy vehicle engine fault detection, and can effectively improve the ability of engine fault detection with good application effect.

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An Intelligent Fault Diagnosis Approach for Multirotor UAVs Based on Deep Neural Network of Multi-Resolution Transform Features

Al-Haddad

Jaber

2023

Drones

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As a modern technological trend, unmanned aerial vehicles (UAVs) are extensively employed in various applications. The core purpose of condition monitoring systems, proactive fault diagnosis, is essential in ensuring UAV safety in these applications. In this research, adaptive health monitoring systems perform blade balancing fault diagnosis and classification. There seems to be a bidirectional unpredictability within each, and this paper proposes a hybrid-based transformed discrete wavelet and a multi-hidden-layer deep neural network (DNN) scheme to compensate for it. Wide-scale, high-quality, and comprehensive soft-labeled data are extracted from a selected hovering quad-copter incorporated with an accelerometer sensor via experimental work. A data-driven intelligent diagnostic strategy was investigated. Statistical characteristics of non-stationary six-leveled multi-resolution analysis in three axes are acquired. Two important feature selection methods were adopted to minimize computing time and improve classification accuracy when progressed into an artificial intelligence (AI) model for fault diagnosis. The suggested approach offers exceptional potential: the fault detection system identifies and predicts faults accurately as the resulting 91% classification accuracy exceeds current state-of-the-art fault diagnosis strategies. The proposed model demonstrated operational applicability on any multirotor UAV of choice.

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Time–Frequency Analysis of Experimental Measurements for the Determination of EMI Noise Generators in Power Converters

Cited by 3 publications

References 54 publications

Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?

Vibration Signal Processing for Multirotor UAVs Fault Diagnosis: Filtering or Multiresolution Analysis?

Fault detection method of new energy vehicle engine based on wavelet transform and support vector machine

An Intelligent Fault Diagnosis Approach for Multirotor UAVs Based on Deep Neural Network of Multi-Resolution Transform Features

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