An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine

Guo, Sumin; Wu, Bo; Zhou, Jingyu; Li, Hongyu; Su, Chunjian; Yuan, Ye; Xu, Ke

doi:10.3390/app10072386

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…The IAF filters out the noise and high-frequency interference terms. Then, the amplitude estimation âm1 and initial-phase estimation φm1 can be obtained by Equation (6). After completing the parameters estimation of frequency component f m1 , it can be removed from the original signal:…”

Section: Inputmentioning

confidence: 99%

“…The fault diagnosis of rolling bearing is necessary for the operation of machinery and to prevent property loss and dangerous situations [1][2][3][4]. Many scholars have studied various fault identification and classification methods [5][6][7]. However, signal acquisition, noise separation, and signal parameter estimation are the most critical segments for fault diagnosis due to the feature signal being typically weak and polluted by noise.…”

Section: Introductionmentioning

confidence: 99%

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A Software Digital Lock-In Amplifier Method with Automatic Frequency Estimation for Low SNR Multi-Frequency Signal

et al. 2022

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In the fault diagnosis field, the fault feature signal is weak and contaminated by the noise. The lock-in amplifier is a useful tool for weak signal detection. Aiming to the amplitude error of the lock-in amplifier caused by frequency deviation between the measured signal and the reference signal, a DFT-based automatic signal frequency estimation method is studied to improve the frequency accuracy of the reference signal. Based on this frequency estimation method, a software digital lock-in amplifier method is proposed to detect the multiple frequencies signals. This proposed method can automatically measure the frequency value of the measured signal without prior frequency information. Then, the reference signals are generated through this frequency value to make the digital lock-in amplifier estimate the amplitude of the measured signal. Moreover, an iterative structure is used to implement the multiple frequencies signal measurement. The frequencies and amplitudes measurement accuracies are tested. Under different SNR conditions, the frequency relative error is less than 0.1%. In addition, the amplitude relative error with different signal frequencies is less than 1.7% when the SNR is −1 dB. This proposed software digital lock-in amplifier method has a higher signal frequency tracking ability and amplitude measurement accuracy.

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Section: Inputmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Software Digital Lock-In Amplifier Method with Automatic Frequency Estimation for Low SNR Multi-Frequency Signal

et al. 2022

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show abstract

“…At the same time, Wang et al [26] used the limit learning machine method to classify the faults of the fuel system. Guo et al [27] combined the circular model with the limit learning machine (ELM) to form a fault diagnosis method for linear analog circuits. Xia et al [28] reported an effective diagnosis method for early faults of water chillers by combining nuclear entropy component analysis (KECA) and voting based ELM (VELM).…”

Section: Introductionmentioning

confidence: 99%

Data Anomaly Diagnosis Method of Temperature Sensor Based on Deep Neural Network

Chen¹

2022

Mobile Information Systems

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As one of the indicators of whether all kinds of machinery and electrical appliances work normally during use, temperature has an important basis for judging the normal work of related machinery. In order to reduce the probability of safety and quality problems caused by inaccurate temperature measurement in the use of these machines and electrical appliances, this paper uses RBF neural network and EEMD modal analysis two deep neural network models to build a deep neural network-based temperature sensor data anomaly diagnosis method. This method first excavates a large number of historical temperature data samples of temperature control sensors in machinery and electrical appliances and analyzes the change law of relevant sample data, so as to build a data anomaly diagnosis database, and then establish a temperature prediction model based on RBF to predict the temperature of electrical appliances and mechanical components; Second, real-time temperature monitoring and sampling are carried out for the normal temperature sensor. Based on the constructed sample database, automatic identification of various abnormal conditions is realized, and the real measured value of the sensor is reconstructed or estimated under abnormal conditions. EEMD feature extraction is carried out for the difference between the predicted temperature and the actual temperature; Finally, the RBF temperature anomaly diagnosis and classification model is constructed, and the feature vector sets are constructed by variance, variance percentage, energy and energy percentage methods, respectively, or jointly, and these vector sets are used as the input of the fault model for temperature anomaly diagnosis and monitoring. Through the diagnosis of the measured temperature sensor data, the established model has a good ability of fault diagnosis and classification.

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“…The above methods cannot reflect the non-stationary characteristics of the signal, resulting in the low separability of the extracted fault features, so there is a large classification error in fault-pattern recognition. Therefore, early soft fault research mainly introduced fuzzy algorithms, wavelet theory, and other means to determine the actual working conditions [7]. Although this algorithm improves the effect of fault diagnosis, some algorithms are seriously affected by the circuit state when analyzing fault characteristics, which makes the performance unstable.…”

Section: Introductionmentioning

confidence: 99%

Research on Analog Circuit Soft Fault Diagnosis Method Based on Mathematical Morphology Fractal Dimension

Yang

et al. 2022

Electronics

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It is difficult for traditional circuit-fault feature-extraction methods to accurately distinguish between nonlinear analog-circuit faults and analog-circuit faults with high fault rates and high diagnostic costs. To solve this problem, this paper proposes a method of mathematical morphology fractal dimension (VMD-MMFD) based on variational mode decomposition for soft-fault feature extraction in analog circuits. First, the signal is decomposed into variational modes to suppress the influence of environmental noise, and multiple high-dimensional eigenmode functions with different center frequencies are obtained. The fractal dimension of the signal feature information component IMF is calculated, and then, KPCA (Kernel Principal Component Analysis) is used to remove the overlapping and redundant parts of the data. The fault set obtained is used as the basis for judging the working state and the fault type of the circuit. The experimental results of the simulation circuits show that this method can be effectively used for circuit-fault diagnosis.

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An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine

Cited by 7 publications

References 21 publications

A Software Digital Lock-In Amplifier Method with Automatic Frequency Estimation for Low SNR Multi-Frequency Signal

A Software Digital Lock-In Amplifier Method with Automatic Frequency Estimation for Low SNR Multi-Frequency Signal

Data Anomaly Diagnosis Method of Temperature Sensor Based on Deep Neural Network

Research on Analog Circuit Soft Fault Diagnosis Method Based on Mathematical Morphology Fractal Dimension

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