Fault detection system of subway sliding plug door based on adaptive EMD method

Wang, Weibo; Liu, Wenxiu; Lin, Chuan; Li, Mingyue; Zheng, Yongkang; Liu, Dong

doi:10.1088/1361-6501/acfb2c

Cited by 4 publications

(1 citation statement)

References 41 publications

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“…The EMD method is effective for handling nonstationary signals. However, its mathematical theory lacks rigor, and using the algorithm directly can result in modal aliasing and boundary effects [9][10][11]. Owing to the limitations of the EMD algorithm, some scholars have proposed the ICEEMDAN algorithm.…”

Section: Introductionmentioning

confidence: 99%

Research on circuit breaker operating mechanism feature extraction method combining ICEEMDAN-MRSVD denoising and VMD-PSE

Yan,

Zhuang,

2024

Meas. Sci. Technol.

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The vibration signal associated with the operating process of circuit breakers(CBs) includes a detailed operating status in the formation of the operating mechanism. To effectively extract the characteristic information of vibration effectively for diagnosis and analysis, a new feature extraction method for the CBs operating mechanism is proposed. First, a new denoising method, the improved complete ensemble empirical mode decomposition with adaptive noise-multi-resolution singular value decomposition (ICEEMDAN-MRSVD), is introduced, which can effectively remove the influence of noise on faults. Then, a quantitative method is proposed to extract the characteristic information of the CB, i.e. the variational mode decomposition (VMD)-power spectrum entropy (PSE) is proposed. By using this method, the difference of CB vibration signals in different fault states can be quantified. Through comparative analysis of different recognition models, experiments show that the support vector machine model based on ICEEMDAN-MRSVD noise reduction and VMD-PSE features has a high recognition accuracy of 98.61%, which has high application value.

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Section: Introductionmentioning

confidence: 99%

Research on circuit breaker operating mechanism feature extraction method combining ICEEMDAN-MRSVD denoising and VMD-PSE

Yan,

Zhuang,

2024

Meas. Sci. Technol.

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A diagnosis method for loss of circulation based on transient-pressure wave analysis and particle swarm optimization

Zhu,

Liu,

et al. 2024

Physics of Fluids

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Lost circulation during operations poses a significant threat to production processes. In the search for an effective detection method, an impulse-response detection method of lost circulation is introduced. This method involves generating transient pressure waves at the wellhead and analyzing their time-frequency domain characteristics to pinpoint location for lost circulation within the wellbore annulus system. Utilizing the data processing capabilities of machine learning models, this study proposes an integrated model for signal feature classification and diagnosis model for lost circulation. Drawing from extensive experimental data, this model integrates laboratory experiments, signal analysis, and machine learning algorithms. Data preprocessing, including wavelet variation and denoising, precedes the application of an enhanced adaptive noise complete ensemble empirical modal decomposition with adapted noise (ICEEMDAN) alongside energy and sample entropy analysis for feature extraction. By establishing a mapping relationship between signal features and lost circulation changes, we develop an improved backpropagation neural network (IBP) model using the particle swarm optimization (PSO) algorithm for diagnosis (PSO-IBP). Comparative analysis of five models reveals compelling results: ① PSO-IBP achieves an average accuracy of 97.60%, with a standard deviation of 0.356; ② diagnosis accuracy for every lost circulation scenario exceeds 92%, outperforming other models in precision, recall, and F-Score; ③ even with limited training data, PSO-IBP maintains 84% accuracy, demonstrating superior performance. Further analysis highlights the efficacy of PSO-IBP, especially when leveraging ICEEMDAN for signal feature extraction, in accurately diagnosing lost circulation.

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Damage-related imbalance identification for UAV composite propeller blades based on bidirectional temporal convolutional network and a flexible sensing system

Gu,

Zhang,

Zhu

et al. 2024

Meas. Sci. Technol.

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Damage to the composite propeller blades could lead to rotational imbalance, which seriously affects the operational safety of unmanned aerial vehicles (UAVs), therefore, a novel method combining the Teager energy operator and bidirectional temporal convolutional network is proposed for detecting, localizing, and quantifying the damage-related imbalance in the blades. A flexible sensing system that contains MEMS accelerometers, signal conditioning, and wireless transmission is integrated with the composite propeller for in-situ signal acquisition of the propeller blades. Teager energy operator (TEO) is applied to demodulate and enhance the pulse compositions in vibration signals and singular value decomposition (SVD) is employed to suppress random noise, resulting in denoised Teager energy spectrums for model input. Temporal convolutional network (TCN) has been widely used in sequence signal modeling because the causal dilated convolution could learn the context information of sequence signals while maintaining the advantages of parallel computing. To fully extract the signal features, bidirectional temporal convolutional network (BiTCN) models are established to learn both the forward and backward signal features. Experimental verification results show that the proposed method detects the existence of imbalance with 100% accuracy, and the accuracies of localization and quantization are 99.65% and 98.61%, respectively, which are much higher than those of the models with the original signal as input. In addition, compared with the other four different algorithms, BiTCN is superior in terms of convergence speed and prediction accuracy.

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Fault detection system of subway sliding plug door based on adaptive EMD method

Cited by 4 publications

References 41 publications

Research on circuit breaker operating mechanism feature extraction method combining ICEEMDAN-MRSVD denoising and VMD-PSE

Research on circuit breaker operating mechanism feature extraction method combining ICEEMDAN-MRSVD denoising and VMD-PSE

A diagnosis method for loss of circulation based on transient-pressure wave analysis and particle swarm optimization

Damage-related imbalance identification for UAV composite propeller blades based on bidirectional temporal convolutional network and a flexible sensing system

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