A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

Aizenberg, Igor; Belardi, Riccardo; Bindi, Marco; Grasso, Francesco; Manetti, S.; Luchetta, Antonio; Piccirilli, Maria Cristina

doi:10.3390/electronics10030349

Cited by 18 publications

(17 citation statements)

References 40 publications

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“…Various machine learning approaches such as neural networks, support vector machines, Naïve Bayes classifier, etc., have been used for fault diagnosis under the broad umbrella of data-driven methods. Neural-network-based fault-diagnosis approaches [13,14] have included, for feature generation: kurtosis and entropy [15], wavelet transforms [16], and fractional wavelet transforms [17]; and for dimensionality reduction: kernel PCA (kPCA) [16,17]. Support vector machine (SVM)-based [18] fault-diagnosis approaches have further included, for feature generation: fractional Fourier transform [19], cross-wavelet transform [20,21], deep belief networks (DBN) [22,23], and empirical mode decomposition [24]; for dimensionality reduction: parametric t-SNE [20] and principal component analysis [21]; and for SVM hyperparameter optimization: the double-chains quantum genetic algorithm [24], the fruitfly algorithm [25], the barnacles mating optimizer algorithm [26], and the firefly algorithm [27].…”

Section: Introductionmentioning

confidence: 99%

Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

2022

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Analog circuits are a critical part of industrial electronics and systems. Estimates in the literature show that, even though analog circuits comprise less than 20% of all circuits, they are responsible for more than 80% of faults. Hence, analog circuit fault diagnosis and isolation can be a valuable means of ensuring the reliability of circuits. This paper introduces a novel technique of learning time–frequency representations, using learnable wavelet scattering networks, for the fault diagnosis of circuits and rotating machinery. Wavelet scattering networks, which are fixed time–frequency representations based on existing wavelets, are modified to be learnable so that they can learn features that are optimal for fault diagnosis. The learnable wavelet scattering networks are developed using the genetic algorithm-based optimization of second-generation wavelet transform operators. The simulation and experimental results for the diagnosis of analog circuit faults demonstrates that the developed diagnosis scheme achieves greater fault diagnosis accuracy than other methods in the literature, even while considering a larger number of fault classes. The performance of the diagnosis scheme on benchmark datasets of bearing faults and gear faults shows that the developed method generalizes well to fault diagnosis in multiple domains and has good transfer learning performance, too.

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Section: Introductionmentioning

confidence: 99%

Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

2022

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“…Based on the system requirements defined by the user, this step divides the user requirements into module-level design constraints employing system design formulas and user preferences, such as those pertaining to occupation and energy efficiency. In this step, we perform supervised learning based on artificial neural networks (ANNs) [23][24][25] to characterize the regression model.…”

Section: Step 2: Creating Sub-module With User Intentmentioning

confidence: 99%

Regression Model-Based AMS Circuit Optimization Technique Utilizing Parameterized Operating Condition

2022

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An analog and mixed-signal (AMS) circuit that draws on machine learning while using a regression model differs in terms of the design compared to more sophisticated circuit designs. Technology structures that are more advanced than conventional CMOS processes, specifically the fin field-effect transistor (FinFET) and silicon-on-insulator (SOI), have been proposed to provide the higher computation performance required to meet various design specifications. As a result, the latest research on AMS design optimization has enabled enormous resource savings in AMS design procedures but remains limited with regard to reflecting the intended operating conditions in the design parameters. Hereby, we propose what is termed a supervised learning artificial neural network (ANN) as a means by which to define an AMS regression model. This approach allows for rapid searches of complex design dimensions, including variations in performance metrics caused by process–voltage–temperature (PVT) changes. The method also reduces the considerable computation expense compared to that of simulation-program-with-integrated-circuit-emphasis (SPICE) simulations. Hence, the proposed AMS circuit design flow generates highly promising output to meet target requirements while showing an excellent ability to match the design target performance. To verify the potential and promise of our design flow, a successive approximation register analog-to-digital converter (SAR ADC) is designed with a 14 nm process design kit. In order to show the maximum single ADC performance (6-bit∼8-bit resolution and few GS/s conversion speed), we have set three different ADC performance targets. Under all SS/TT/FF corners, ±6.25% supply voltage variation, and temperature variation from −40 ∘C to 80 ∘C, the designed SAR ADC using our proposed AMS circuit optimization flow yields remarkable figure-of-merit energy efficiency (approximately 15 fJ/conversion step).

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“…These components are the main subjects of the prognostic analysis, and their variations with respect to the nominal values are used as indexes of the state converter of health. In fact, when a malfunction occurs on a passive component, its value changes; this introduces a variation of the working point [21,22] and could produce catastrophic consequences. To make the simulations as close as possible to the real functioning of the converter, the parasites of the real active and passive components are considered in Simulink.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Monitoring of DC-DC Converters in Photovoltaic Applications

et al. 2022

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In this paper, a monitoring method for DC-DC converters in photovoltaic applications is presented. The primary goal is to prevent catastrophic failures by detecting malfunctioning conditions during the operation of the electrical system. The proposed prognostic procedure is based on machine learning techniques and focuses on the variations of passive components with respect to their nominal range. A theoretical study is proposed to choose the best measurements for the prognostic analysis and adapt the monitoring method to a photovoltaic system. In order to facilitate this study, a graphical assessment of testability is presented, and the effects of the variable solar irradiance on the selected measurements are also considered from a graphical point of view. The main technique presented in this paper to identify the malfunction conditions is based on a Multilayer neural network with Multi-Valued Neurons. The performances of this classifier applied on a Zeta converter are compared to those of a Support Vector Machine algorithm. The simulations carried out in the Simulink environment show a classification rate higher than 90%, and this means that the monitoring method allows the identification of problems in the initial phases, thus guaranteeing the possibility to change the work set-up and organize maintenance operations for DC-DC converters.

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A Neural Network Classifier with Multi-Valued Neurons for Analog Circuit Fault Diagnosis

Cited by 18 publications

References 40 publications

Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

Learnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery

Regression Model-Based AMS Circuit Optimization Technique Utilizing Parameterized Operating Condition

Machine Learning-Based Monitoring of DC-DC Converters in Photovoltaic Applications

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