Analog circuit soft fault diagnosis utilizing matrix perturbation analysis

Zhang, Tianwen; Li, Tingjun

doi:10.1007/s10470-019-01433-x

Cited by 12 publications

(8 citation statements)

References 32 publications

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“…is imported into the trained classifier to verify the effectiveness of the proposed model. Furthermore, the probability of each fault class of testing sample x i te can be computed according to equation (11), while the class with the maximum probability is assigned as the testing sample. Finally, the whole diagnosis performance can be derived by comparing the assigned class label with the actual class label.…”

Section: Softmax Regression Classifiermentioning

confidence: 99%

“…In this regard, many handcrafted feature extraction methods, including timedomain analysis [7,8], frequency-domain analysis [9] and time-frequency analysis [10], have been extensively investigated. For example, Zhang applied matrix perturbation analysis on a time-domain signal to obtain feature vectors, including the matrix spectral radius and perturbation matrix m1 norm [11]. Gao proposed a feature extraction method based on the PSO algorithm and Wilks Λ-statistic for the selection of analogue circuit frequency feature vectors [12].…”

Section: Introductionmentioning

confidence: 99%

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Robust locally linear embedding and its application in analogue circuit fault diagnosis

He¹,

Yuan

Yin

et al. 2023

Meas. Sci. Technol.

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During long-term operation of analogue circuits, fault diagnosis is important for preventing the occurrence of hazards. However, noise often accompanies sampled signals and makes the task of fault diagnosis more difficult. Therefore, developing a robust feature extraction technique is an indispensable part of fault diagnosis. The locally linear embedding (LLE) algorithm has recently emerged as a promising technique for dimensional reduction and feature extraction because it preserves linear neighbourhoods, and it is quite effective when there is a locally linear dependent structure embedded in fault data. However, LLE is sensitive to noise.Therefore, the maximum correntropy criterion is adopted to resist non-Gaussian noise by seeking the optimal weight coefficient, and a half-quadratic optimization procedure is introduced to address the objective function. Moreover, softmax regression is applied to locate faults.Finally, two typical analogue circuit systems are used to demonstrate the robustness of the modified algorithm to non-Gaussian noise. The experimental results show that the robust LLE algorithm can outperform LLE in the extraction of fault features when there is non-Gaussian noise in the fault signals, and the proposed fault diagnosis method has a better effect in locating faults compared with other feature extraction methods.

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Section: Softmax Regression Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust locally linear embedding and its application in analogue circuit fault diagnosis

He¹,

Yuan

Yin

et al. 2023

Meas. Sci. Technol.

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“…DL is a branch of machine learning that excludes the need to manually extract signal features and provides greater feature extraction and fault identification than conventional machine learning [7,8]. Signal processing methods such as Fast Wavelet Transform [9], FFT [10], Ramanujan-Fourier Transform [11], and matrix perturbation analysis [12], which require extra manual intervention, are difficult to meet the requirements in terms of the accuracy and timeliness of the modern industrial equipment automation diagnosis [13].…”

Section: Introductionmentioning

confidence: 99%

Prior knowledge-based residuals shrinkage prototype networks for cross-domain fault diagnosis

Zheng

et al. 2023

Meas. Sci. Technol.

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In engineering practice, device failure samples are limited in the case of unexpected catastrophic faults, thereby limiting the application of deep learning in fault diagnosis. In this study, we propose a prior knowledge-based residual shrinkage prototype network (PK-RSPN) to resolve the fault diagnosis challenges under limited labeled samples. First, our method combines general supervised learning and metric meta-learning to extract prior knowledge from the labeled source data by utilizing a denoised residual shrinkage network (RSN). Further, the knowledge extracted from the supervised learning is used for prototype metric training to achieve a better feature representation for the fault diagnosis. Finally, our approach outperforms a series of baseline methods in the few-shot cross-domain diagnostic task on the gearbox and bearing datasets. A diagnosis accuracy of more than 95% has been achieved in a variety of working conditions for diagnostic tasks, which is far higher than the existing basic method.

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“…However, in this method, the Monte-Carlo simulation is determined explicitly and an adaptive threshold estimation is required. Other modeling approaches, including the matrix model [8], fuzzy model [9], parity space-based model [10] and the hidden Markov model (HMM) [11], are also combined with various signal processing methods for the fault diagnosis in analog circuits. Yang et al [12] proposed a float encoding genetic algorithm that can model all parameter shifting faults with the grouped crossover, mutation and an integrated selection strategy.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network

Yang¹,

Meng²,

Wang³

2020

IEEE Access

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The present study applies the one-dimensional convolutional neural network (1D-CNN) to propose an intelligent approach of the feature extraction for the analog circuit diagnosis. The raw signals based on various soft faults from the output terminal of the circuit under test (CUT) are collected with appropriate data acquisition system to implement a data-driven fault diagnosis. The data-driven diagnosis process is typically encapsulated in two distinct blocks, including the feature extraction and the classification. In this study, the designed 1D-CNN model efficiently combines the aforementioned two phases into a single diagnosis body with fast learning rate and accurate classification. The main advantages of the 1D-CNN are: 1) it can be directly established to the raw signal with proper training so that it is more applicable in real applications; 2) its compact architecture and configuration has reasonable applicability in complex analog circuits; 3) convolutional kernels guarantee that the hierarchical features can be extracted from raw data with better anti-interference performance. Moreover, since the method can extract high-level features of raw signals, it resolves the necessity to employ other per-processing methods for the hand-crafted feature transformation. The performance of the proposed 1D-CNN model is evaluated through three benchmark circuits on the SIMULINK platform. Obtained results are compared with other intelligent fault diagnosis methods. The experimental results show that the 1D-CNN can be utilized effectively as the feature exactor and faults classifier for analog circuits. INDEX TERMS Analog circuit fault diagnosis, convolutional neural network, data-driven method, faults classification, feature extraction.

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Analog circuit soft fault diagnosis utilizing matrix perturbation analysis

Cited by 12 publications

References 32 publications

Robust locally linear embedding and its application in analogue circuit fault diagnosis

Robust locally linear embedding and its application in analogue circuit fault diagnosis

Prior knowledge-based residuals shrinkage prototype networks for cross-domain fault diagnosis

Data-Driven Feature Extraction for Analog Circuit Fault Diagnosis Using 1-D Convolutional Neural Network

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