Failure-rate Comparison of Single-ended and Double-ended Forward Converter by means of Fault-tree Analysis

Kwak, Yun-Gi; Heo, Dae-ho; Lee, Byoung-Hee; Kang, Feiyu

doi:10.1109/isie45063.2020.9152405

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…In this section, the mean time between failures (MTBF) and failure-rate, which are representative indicators of reliability, are derived through part count failure analysis (PCA) and fault-tree analysis (FTA) [27,28,[34][35][36].…”

Section: Reliability Analysis Of Forward Convertermentioning

confidence: 99%

Reliability and Characteristic Analysis Considering the Circuit Structure and Operation Risk of Forward Converters

2021

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The forward converter is divided into two circuit topologies depending on the method of resetting the energy stored in the magnetizing inductance of the transformer: a single-ended structure using a reset circuit and a double-ended structure called a two-switch forward converter and, accordingly, a difference occurs in the operational mode. Generally, forward converters are designed with low power of less than 500 W and are sold at low prices, so it is common to replace them in case of failure. However, in terms of environmental conservation through resource saving, it is desirable to design with as high reliability as possible. In this paper, reliability is analyzed for the two representative circuit topologies of forward converters. The conventional PCA (part count failure analysis) method determines the failure rate only by the number of circuit components constituting the converter and the correlation between the components, but the FTA (fault-tree analysis) method can additionally consider the operation risk of the converter according to the circuit configuration and operation. Therefore, it is possible to analyze the failure rate according to the operating characteristics of the two converters from the difference in the failure rate obtained by the two methods. In addition, the two converters were manufactured as a 300 W prototype, respectively, and the volume, weight, and power loss were compared, and the economic feasibility was analyzed based on the cost model.

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Section: Reliability Analysis Of Forward Convertermentioning

confidence: 99%

Reliability and Characteristic Analysis Considering the Circuit Structure and Operation Risk of Forward Converters

2021

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Online Monitoring of Power Converter Degradation Using Deep Neural Network

et al. 2021

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Power semiconductor devices in the power converters used for motor drives are susceptible to wear-out and failure, especially when operated in harsh environments. Therefore, detection of degradation of power devices is crucial for ensuring the reliable performance of power converters. In this paper, a deep learning approach for online classification of the health states of the snubber resistors in the Insulated Gate Bipolar Transistors (IGBTs) in a three-phase Brushless DC (BLDC) motor drive is proposed. The method can locate one out of the six IGBTs experiencing a snubber resistor degradation problem by measuring the voltage waveforms of the three shunt resistors using voltage sensors. The range of the degradation of the snubber resistors for successful classification is also investigated. The off-the-shelf deep Convolutional Neural Network (CNN) architecture ResNet50 is used for transfer learning to determine which snubber resistor has degraded. The dataset for evaluating the above classification scheme of IGBT degradation is obtained by measuring the shunt voltage waveforms with varying snubber resistance and reference current. Then, the three-phase voltage waveforms are converted into greyscale images and RGB spectrogram images, which are later fed into the deep CNN. Experiments are carried out on the greyscale image dataset and the spectrogram image dataset using four-fold cross-validation. The results show that the proposed scheme can classify seven classes (one class for normal condition and six classes for abnormal condition in one of the six IGBTs in a three-phase BLDC drive) with over 95% average accuracy within a specific range of snubber resistance. Using grayscale images and using spectrogram-based RGB images yields similar accuracy.

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Failure-rate Comparison of Single-ended and Double-ended Forward Converter by means of Fault-tree Analysis

Cited by 2 publications

References 6 publications

Reliability and Characteristic Analysis Considering the Circuit Structure and Operation Risk of Forward Converters

Reliability and Characteristic Analysis Considering the Circuit Structure and Operation Risk of Forward Converters

Online Monitoring of Power Converter Degradation Using Deep Neural Network

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