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28 pages, 11355 KB

Open AccessArticle

Research on Fault Diagnosis of UAV Rotor Motor Bearings Based on WPT-CEEMD-CNN-LSTM

by Xianyi Shang, Wei Li, Fang Yuan, Haifeng Zhi, Zhilong Gao, Min Guo and Bo Xin

Machines 2025, 13(4), 287; https://doi.org/10.3390/machines13040287 - 31 Mar 2025

Cited by 5 | Viewed by 1383

To address the challenge of extracting adaptive fault features for unmanned aerial vehicle (UAV) rotor motor bearings and to meet the high accuracy requirements of bearing fault diagnosis, this paper proposes a neural network-based bearing fault diagnosis method using WPT-CEEMD-CNN-LSTM. Initially, the method applies multiple noise reduction processes to the original vibration signals and enhances their time–frequency resolution through Wavelet Packet Transform (WPT) and Complete Ensemble Empirical Mode Decomposition (CEEMD). This effectively removes noise and generates a high-quality dataset. Subsequently, a Convolutional Neural Network (CNN) is employed to automatically extract deep features, while a Long Short-Term Memory (LSTM) network is used for the time-series modeling, thereby constructing an accurate rotor motor bearing fault diagnosis model. The experimental results demonstrate that the fault diagnosis accuracy of this method reaches 96.67%, which is significantly higher than that of the traditional CNN (85%), LSTM (51.33%), and the CEEMD-CNN-LSTM model with single-signal noise reduction (77.33%). This method also exhibits stronger fault identification and generalization capabilities. This study confirms the effectiveness of combining WPT-CEEMD with CNN-LSTM deep learning techniques for UAV bearing fault diagnosis, providing a high-precision and stable diagnostic solution for UAV health monitoring. Full article

(This article belongs to the Section Machines Testing and Maintenance)

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23 pages, 10903 KB

Open AccessArticle

Noise Reduction Based on a CEEMD-WPT Crack Acoustic Emission Dataset

by Yongfeng Zhao, Yunrui Ma, Junli Du, Chaohua Wang, Dawei Xia, Weifeng Xin, Zhenyu Zhan, Runfeng Zhang and Jiangyi Chen

Appl. Sci. 2023, 13(18), 10274; https://doi.org/10.3390/app131810274 - 13 Sep 2023

Cited by 11 | Viewed by 2174

Abstract

In order to solve the noise reduction problem of acoustic emission signals with cracks, a method combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and wavelet packet (WPT) is proposed and named CEEMD-WPT. Firstly, the single Empirical Mode Decomposition (EMD) used in the traditional CEEMD is improved into the WPT-EMD with a more stable noise reduction effect. Secondly, after decomposition, the threshold value of the correlation coefficient is determined for the Intrinsic Mode Function (IMF), and the low correlation component is further processed by WPT. In addition, in order to solve the problem that it is difficult to quantify the real signal noise reduction effect, a new quantization index “principal interval coefficient (PIC)” is designed in this paper, and its reliability is verified through simulation experiments. Finally, noise reduction experiments are carried out on the real crack acoustic emission dataset consisting of tensile, shear, and mixed signals. The results show that CEEMD-WPT has the highest number of signals with a principal interval coefficient of 0–0.2, which has a better noise reduction effect compared with traditional CEEMD and Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). Moreover, the statistical variance of CEEMD-WPT is evidently one order of magnitude smaller than that of CEEMD, so it has stronger stability. Full article

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14 pages, 5013 KB

Open AccessArticle

Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

by Yan Yan and Hongyan Xing

J. Mar. Sci. Eng. 2021, 9(6), 651; https://doi.org/10.3390/jmse9060651 - 12 Jun 2021

Cited by 9 | Viewed by 3113

Abstract

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter. Full article

(This article belongs to the Special Issue Artificial Intelligence in Marine Science and Engineering)

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13 pages, 2470 KB

Open AccessCommunication

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

by Kwok Tai Chui, Brij B. Gupta, Ryan Wen Liu and Pandian Vasant

Sensors 2021, 21(9), 3133; https://doi.org/10.3390/s21093133 - 30 Apr 2021

Cited by 27 | Viewed by 3002

Abstract

Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD–WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD–WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly. Full article

(This article belongs to the Special Issue Smart Sensor for Smartgrids and Microgrids)

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