Adaptive multiscale and dual subnet convolutional auto-encoder for intermittent fault detection of analog circuits in noise environment

Fang, Xinsheng; Qu, Jianfeng; Chai, Yi; Liu, Bowen

doi:10.1016/j.isatra.2022.10.031

Cited by 12 publications

(5 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The data were randomly selected for testing. After completing the training set partitioning, the model enters the pre-training phase, and the main parameters of ECWGEO are set as follows: the number of filters in the first convolutional layer , the number of filters in the second convolutional layer (64-256), the size of the convolutional kernel (2)(3)(4)(5)(6)(7)(8), the learning rate (0.0001-0.1) and dropout rate (0-0.5), and the population size is set to 30 and maximum iteration is set to 100. Twenty epochs are used for model training, the batch size is 32, and the fitness function is set as shown in Equation (27).…”

Section: Methodsmentioning

confidence: 99%

“…is the normal vector and the hyperplane in which the cruise vector is located and can be computed using Equation (5).…”

Section: Exploitation and Explorationmentioning

confidence: 99%

“…Furthermore, statistics have shown that although 80% of the board cards are digital, most failures occur in the analog section [ 4 ]. Therefore, the research on analog-circuit-fault-diagnosis methods has become a hot topic, which is of great importance for improving the reliability and operational efficiency of high-safety equipment, as well as efficient health management, remote operation, and digital overhaul [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Exploration into the Fault Diagnosis of Analog Circuits Using Enhanced Golden Eagle Optimized 1D-Convolutional Neural Network (CNN) with a Time-Frequency Domain Input and Attention Mechanism

Gao,

Guo,

Yuan

et al. 2024

Sensors

View full text Add to dashboard Cite

With the continuous operation of analog circuits, the component degradation problem gradually comes to the forefront, which may lead to problems, such as circuit performance degradation, system stability reductions, and signal quality degradation, which could be particularly evident in increasingly complex electronic systems. At the same time, due to factors, such as continuous signal transformation, the fluctuation of component parameters, and the nonlinear characteristics of components, traditional fault localization methods are still facing significant challenges when dealing with large-scale complex circuit faults. Based on this, this paper proposes a fault-diagnosis method for analog circuits using the ECWGEO algorithm, an enhanced version of the GEO algorithm, to de-optimize the 1D-CNN with an attention mechanism to handle time–frequency fusion inputs. Firstly, a typical circuit-quad op-amp dual second-order filter circuit is selected to construct a fault-simulation model, and Monte Carlo analysis is used to obtain a large number of samples as the dataset of this study. Secondly, the 1D-CNN network structure is improved for the characteristics of the analog circuits themselves, and the time–frequency domain fusion input is implemented before inputting it into the network, while the attention mechanism is introduced into the network. Thirdly, instead of relying on traditional experience for network structure determination, this paper adopts a parameter-optimization algorithm for network structure optimization and improves the GEO algorithm according to the problem characteristics, which enhances the diversity of populations in the late stage of its search and accelerates the convergence speed. Finally, experiments are designed to compare the results in different dimensions, and the final proposed structure achieved a 98.93% classification accuracy, which is better than other methods.

show abstract

Section: Methodsmentioning

confidence: 99%

“…is the normal vector and the hyperplane in which the cruise vector is located and can be computed using Equation (5).…”

Section: Exploitation and Explorationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Exploration into the Fault Diagnosis of Analog Circuits Using Enhanced Golden Eagle Optimized 1D-Convolutional Neural Network (CNN) with a Time-Frequency Domain Input and Attention Mechanism

Gao,

Guo,

Yuan

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…Unsupervised learning means that in the training stage, the training dataset of the model only contains input features, without corresponding output tags. Auto-Encoder (AE) [40,41] is an unsupervised neural network that can extract features to reconstruct data. Its basic structure consists of an encoder and decoder.…”

Section: Data Cleaningmentioning

confidence: 99%

Evaluation of Energy Utilization Efficiency and Optimal Energy Matching Model of EAF Steelmaking Based on Association Rule Mining

Yang,

Li,

et al. 2024

Metals

View full text Add to dashboard Cite

In the iron and steel industry, evaluating the energy utilization efficiency (EUE) and determining the optimal energy matching mode play an important role in addressing increasing energy depletion and environmental problems. Electric Arc Furnace (EAF) steelmaking is a typical short crude steel production route, which is characterized by an energy-intensive fast smelting rhythm and diversified raw charge structure. In this paper, the energy model of the EAF steelmaking process is established to conduct an energy analysis and EUE evaluation. An association rule mining (ARM) strategy for guiding the EAF production process based on data cleaning, feature selection, and an association rule (AR) algorithm was proposed, and the effectiveness of this strategy was verified. The unsupervised algorithm Auto-Encoder (AE) was adopted to detect and eliminate abnormal data, complete data cleaning, and ensure data quality and accuracy. The AE model performs best when the number of nodes in the hidden layer is 18. The feature selection determines 10 factors such as the hot metal (HM) ratio and HM temperature as important data features to simplify the model structure. According to different ratios and temperatures of the HM, combined with k-means clustering and an AR algorithm, the optimal operation process for the EUE in the EAF steelmaking under different smelting modes is proposed. The results indicated that under the conditions of a low HM ratio and low HM temperature, the EUE is best when the power consumption in the second stage ranges between 4853 kWh and 7520 kWh, the oxygen consumption in the second stage ranges between 1816 m3 and 1961 m3, and the natural gas consumption ranges between 156 m3 and 196 m3. Conversely, under the conditions of a high HM ratio and high HM temperature, the EUE tends to decrease, and the EUE is best when the furnace wall oxygen consumption ranges between 4732 m3 and 5670 m3, and the oxygen consumption in the second stage ranges between 1561 m3 and 1871 m3. By comparison, under different smelting modes, the smelting scheme obtained by the ARM has an obvious effect on the improvement of the EUE. With a high EUE, the improvement of the A2B1 smelting mode is the most obvious, from 24.7% to 53%. This study is expected to provide technical ideas for energy conservation and emission reduction in the EAF steelmaking process in the future.

show abstract

“…Fang et al proposed an adaptive multiscale and dual subnet convolutional auto-encoder (AMDSCAE) to detect intermittent faults in analog circuits in noisy environments. Although the proposed AMDSCAE achieves high accuracy in the detection of intermittent faults in analog circuits, it is slightly more expensive than other networks in terms of computational cost [ 20 ]. Gao et al proposed an automatic fault detection method for seismic images based on a novel multiscale attention convolutional neural network (MACNN), which can effectively extract fault features and automate fault detection by introducing a multiscale attentional mechanism [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method of Four-Mass Vibration MEMS Gyroscope Based on ResNeXt-50 with Attention Mechanism and Improved EWT Algorithm

Wang²,

et al. 2023

Micromachines

View full text Add to dashboard Cite

In this paper, a fault identification algorithm combining a signal processing algorithm and machine learning algorithm is proposed, using a four-mass vibration MEMS gyroscope (FMVMG) for signal acquisition work, constructing a gyroscope fault dataset, and performing the model training task based on this dataset. Combining the improved EWT algorithm with SEResNeXt-50 reduces the impact of white noise in the signal on the identification task and significantly improves the accuracy of fault identification. The EWT algorithm is a wavelet analysis algorithm with adaptive wavelet analysis, which can significantly reduce the impact of boundary effects, and has a good effect on decomposition of signal segments with short length, but a reconstruction method is needed to effectively separate the noise signal and effective signal, and so this paper uses multiscale permutation entropy for calculation. For the reason that the neural network has a better ability to characterize high-dimensional signals, the one-dimensional signal is reconstructed into a two-dimensional image signal and the signal features are extracted. Then, the constructed image signals are fed into the SEResNeXt-50 network, and the characterization ability of the model is further improved in the network with the addition of the Squeeze-and-Excitation module. Finally, the proposed model is applied to the FMVMG fault dataset and compared with other models. In terms of recognition accuracy, the proposed method improves about 30.25% over the BP neural network and about 1.85% over ResNeXt-50, proving the effectiveness of the proposed method.

show abstract

Adaptive multiscale and dual subnet convolutional auto-encoder for intermittent fault detection of analog circuits in noise environment

Cited by 12 publications

References 29 publications

An Exploration into the Fault Diagnosis of Analog Circuits Using Enhanced Golden Eagle Optimized 1D-Convolutional Neural Network (CNN) with a Time-Frequency Domain Input and Attention Mechanism

An Exploration into the Fault Diagnosis of Analog Circuits Using Enhanced Golden Eagle Optimized 1D-Convolutional Neural Network (CNN) with a Time-Frequency Domain Input and Attention Mechanism

Evaluation of Energy Utilization Efficiency and Optimal Energy Matching Model of EAF Steelmaking Based on Association Rule Mining

A Fault Diagnosis Method of Four-Mass Vibration MEMS Gyroscope Based on ResNeXt-50 with Attention Mechanism and Improved EWT Algorithm

Contact Info

Product

Resources

About