Soft Fault Diagnosis of Analog Circuit Based on EEMD and Improved MF-DFA

Lu, Xinmiao; Lu, Zihan; Wu, Qiong; Wang, Jiaxu; Yang, Cunfang; Sun, Shuhui; Shao, Dan; Liu, Kaiyi

doi:10.3390/electronics12010114

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…EEMD was proposed to overcome the mode confusion problem of empirical mode decomposition (EMD), its basic principle is that Gaussian white noise is added into raw signals, and signals can be automatically distributed to the appropriate reference scale. Terefore, EEMD can achieve the better time-frequency analysis of nonstationary vibration signals from bearings [32,33]. Te procedure of EEMD is illustrated in Figure 1, and the specifc implementation process of EEMD is as follows [34]:…”

Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

A New Framework Based on Supervised Joint Distribution Adaptation for Bearing Fault Diagnosis across Diverse Working Conditions

Liu,

Dong

2024

Shock and Vibration

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To address the degradation of diagnostic performance due to data distribution differences and the scarcity of labeled fault data, this study has focused on transfer learning-based cross-domain fault diagnosis, which attracts considerable attention. However, deep transfer learning-based methods often present a challenge due to their time-consuming and costly nature, particularly in tuning hyperparameters. For this issue, on the basis of classical features-based transfer learning method, this study introduces a new framework for bearing fault diagnosis based on supervised joint distribution adaptation and feature refinement. It first utilizes ensemble empirical mode decomposition to process raw signals, and statistical features extraction is implemented. Then, a new feature refinement module is designed to refine domain adaptation features from high-dimensional feature set by evaluating the fault distinguishability and working-condition invariance of feature data. Next, it proposes a supervised joint distribution adaptation method to conduct improved joint distribution alignment that preserves neighborhood relationships within a manifold subspace. Finally, an adaptive classifier is trained to predict fault labels of feature data across varying working conditions. To prove the cross-domain fault diagnosis performance and superiority of the proposed methods, two bearing datasets are applied for experiments, and the experimental results verify that the model built by the proposed framework can achieve desirable diagnosis performance under different working conditions and that it apparently outperforms comparative models.

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Section: Ensemble Empirical Mode Decomposition (Eemd)mentioning

confidence: 99%

A New Framework Based on Supervised Joint Distribution Adaptation for Bearing Fault Diagnosis across Diverse Working Conditions

Liu,

Dong

2024

Shock and Vibration

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“…However, analog circuits consisting of components with nonlinearities and tolerances make fault diagnosis very difficult due to the insufficient number of measurable nodes and measurement uncertainty [10]. Therefore, realizing component-level soft fault diagnosis is still a challenging task, and soft fault diagnosis of analog circuits is still a difficult and hot research topic at this stage [11][12].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of analogue circuits based on artificial intelligence algorithms

2023

Applied Mathematics and Nonlinear Sciences

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The fault diagnosis problem of analog circuits has been paid more and more attention, and the realization of circuit fault feature extraction and pattern classification are two of the key problems. This paper first combines wavelet packet and energy entropy to design a fault feature extraction method based on wavelet packet entropy to solve the problem of analog circuit fault feature extraction. Secondly, the principle of SVM and particle swarm optimization algorithm are combined to design the fault diagnosis process of analog circuits based on an artificial intelligence algorithm. Finally, take the sallen-key circuit as an example to analyze the effectiveness of the wavelet packet entropy algorithm for fault feature extraction and analyze the effectiveness of this paper’s method based on the analog circuit of Wen’s bridge oscillation circuit. The results show that after wavelet packet entropy extraction of the feature vector values is less than 0.001, and the accuracy of the extraction is more than 0.98, the best parameters of the optimization is (182.4, 0.05), the false alarm rate of the fault diagnostic method is 0, the misdiagnosis rate is 0.08, the omission rate is 0, and the correctness rate of the diagnosis of the fault is 0.92. Based on this research is able to carry out the fault diagnosis of the analog circuit.

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“…5, during the training phase of the model. From the diagonal elements of the of Matlab is utilized to construct the simulation model of the drive circuit.…”

mentioning

confidence: 99%

A Fault Diagnosis Method for Rectifier-Filter Circuit Integrating EEMD Algorithm and Transformer Network

Wang,

He,

Zhou

et al. 2024

J. Phys.: Conf. Ser.

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Rectifier-filter circuit, as a critical component of the drive circuit in instrumentation and control systems of nuclear power plants, can convert the 50 Hz AC into the smooth DC. Thus, it plays a vital role in the power control of reactors. However, the weak waveform anomalies of soft faults in the rectifier-filter circuit make fault feature extraction difficult. Therefore, in this article, an ensemble empirical modal decomposition (EEMD) algorithm is employed to decompose the signal mode components in the monitor data of the rectifier-filter circuit. The weak waveform anomalies are indirectly enhanced by the IMF and residual components. Subsequently, the Transformer network is utilized to construct the feature extractor. With the advantage of multi-head attention (MHA) mechanism in the Transformer network, the multi-directional, multi-scale, and highly sensitive long-range time-dependent features in the EEMD feature data are extracted. Then, a deep Softmax classifier is adopted to reduce the dimensionality and diagnose the soft faults of the rectifier-filter circuit. Finally, a fault simulation model of the rectifier-filter circuit is constructed and the condition monitor data are collected. The effectiveness and diagnosis accuracy of the proposed method are verified by a real case experiment and some comparative methods.

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Soft Fault Diagnosis of Analog Circuit Based on EEMD and Improved MF-DFA

Cited by 5 publications

References 11 publications

A New Framework Based on Supervised Joint Distribution Adaptation for Bearing Fault Diagnosis across Diverse Working Conditions

A New Framework Based on Supervised Joint Distribution Adaptation for Bearing Fault Diagnosis across Diverse Working Conditions

Fault diagnosis of analogue circuits based on artificial intelligence algorithms

A Fault Diagnosis Method for Rectifier-Filter Circuit Integrating EEMD Algorithm and Transformer Network

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