BCSSA-VMD and ICOA-ELM based fault diagnosis method for analogue circuits

Analog circuits are an important component of integrated circuit systems, and circuit systems are the foundation for ensuring the normal operation of electronic devices. Therefore, it is necessary to efficiently diagnose and maintain faults in analog circuits. However, due to the tolerance, high non...

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Veröffentlicht in:Analog integrated circuits and signal processing Jg. 123; H. 2; S. 20
Hauptverfasser: You, Dazhang, Liu, Shan, Yuan, Ye, Zhang, Yepeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Springer US 01.05.2025
Springer Nature B.V
Schlagworte:
Accuracy
Algorithms
Analog circuits
Bandpass filters
Bandwidths
Circuits
Circuits and Systems
Classification
Data processing
Decision trees
Decomposition
Electrical Engineering
Electronic devices
Engineering
Entropy
Fault diagnosis
Feature extraction
Fourier transforms
Integrated circuits
Machine learning
Neural networks
Optimization
Pattern recognition
Search algorithms
Signal,Image and Speech Processing
Support vector machines
Wavelet transforms
Fault diagnosis
Crayfish algorithm
Sparrow algorithm
Analogue circuits
Extreme learning machine
VMD algorithm
ISSN:0925-1030, 1573-1979
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Zusammenfassung:Analog circuits are an important component of integrated circuit systems, and circuit systems are the foundation for ensuring the normal operation of electronic devices. Therefore, it is necessary to efficiently diagnose and maintain faults in analog circuits. However, due to the tolerance, high nonlinearity, and susceptibility to environmental interference of analog circuit components, the development of related research on fault diagnosis has been hindered, and it cannot meet the current practical requirements for high safety and reliability of electronic devices. With the continuous increase in circuit scale and integration level, how to effectively and as much as possible extract more discriminative fault features is the key research direction of analog circuit fault diagnosis. Therefore, this article proposes a variational model decomposition (VMD) feature extraction method that combines Butterfly and Cauchy Sparrow search algorithms (BCSSA) and relies on an improved crayfish optimization algorithm (COA) to optimize the Extreme Learning Machine (ELM). Decomposition (VMD) feature extraction method, and rely on Improved Crayfish Optimization Algorithm (COA) Optimized Extreme Learning Machine (ELM) to complete the classification of faults. Firstly, the BCSSA algorithm is used to optimize the number of VMD decomposition modes K and the penalty factor α to achieve the optimal VMD decomposition of the original fault signal, obtain a series of Intrinsic Mode Function (IMF) and calculate its envelope entropy, determine the optimal IMF component by selecting the IMF component with the lowest envelope entropy., and calculate its time-domain parameter, then normalize and reduce the dimensionality to construct the vector that contains the characteristics of the fault. The normalized dimensionality reduction process constitutes the fault feature vector; secondly, the ICOA algorithm is introduced to optimize the ELM; Ultimately, the fault feature vector is fed into the ELM to acquire the fault diagnosis results. The simulation test examples of the Sallen-Key bandpass filter circuit and the Four-op-amp circuit show that the accuracy of the proposed improved VMD and ELM fault diagnosis method is as high as 99.68%.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0925-1030
1573-1979
DOI:10.1007/s10470-025-02360-w