BP neural network for non-invasive IGBT junction temperature online detection

Liu, Li; Peng, Qiyu; Jiang, Huaping; Li, Ran; Wang, Yan; Du, Chao; Chen, Jian; Zhou, Haoming; Chen, Yang; Peng, Zhiyuan

doi:10.1016/j.microrel.2022.114882

Cited by 12 publications

(1 citation statement)

References 18 publications

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“…The study of translation quality assessment and its model can strengthen the standardization of translation behavior, improve the quality of translation and translation evaluation, and improve the efficiency of translator training [10][11]. The traditional assessment criteria are too conservative and inefficient, and the use of gray prediction and BP neural network in English-Chinese translation quality assessment can be more efficient in assessing translation [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Research on the Application of Gray Prediction and BP Neural Network in English-Chinese Translation Quality Assessment

2024

Applied Mathematics and Nonlinear Sciences

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Gray prediction model and BP neural network are increasingly used in many fields as important prediction and analysis tools. The article first elaborates on the related algorithm of the gray prediction method. It constructs the gray prediction model, then constructs the BP neural network, and builds the gray BP neural network translation quality assessment model through the modeling of the simulation of the artificial neural network, and finally takes eight postgraduate students of translation majors of Beijing Foreign Studies University as the subjects, and the eight students translate two articles with similar word counts and difficulties, and score the translation quality of the subjects, and reasonably evaluate and scientifically predict these translations in order to validate the grey translation quality of subjects. The quality of translation is scored, and these translations are reasonably evaluated and scientifically predicted to verify the role and effect of the gray BP neural network in the quality of English-Chinese translation. The study concludes that the results based on the gray prediction model are that the prediction results are in the error range of 0%–7% compared with the original data, and the overall error is small. The prediction results of the compensatory fuzzy neural network prediction model for all 8 subjects before and after translation were more accurate than those predicted by the traditional fuzzy neural network method, with a good fit to the actual monitoring data and a small relative error. Only one subject out of the 8 spent more time on Chapter B than on Chapter A. Chapter B was more time-consuming than Chapter A. The time spent on Chapter A was more than the time spent on Chapter A. Chapter B was more time-consuming than Chapter A. Chapter B was more time-consuming than Chapter A. One subject spent equal time on both articles. The remaining 6 subjects did not spend as much time on chapter B as they did on chapter A.

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Section: Introductionmentioning

confidence: 99%

Research on the Application of Gray Prediction and BP Neural Network in English-Chinese Translation Quality Assessment

2024

Applied Mathematics and Nonlinear Sciences

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IGBT Junction Temperature Estimation Based on External Parameters of Multiple Drive Devices in Practical Industrial Scenario

Su,

Jiang,

Huang

2024

Circuit Theory & Apps

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The insulated gate bipolar transistor (IGBT) is one of the most important power semiconductor devices in power electronics and is also prone to failure. High junction temperature and junction temperature fluctuation of IGBT are the main causes of IGBT module aging failure. The high‐precision monitoring of the junction temperature of the IGBT module is a prerequisite for IGBT life prediction, which is crucial for reducing maintenance costs and improving equipment reliability. Therefore, an IGBT junction temperature estimation method based on long short‐term memory (LSTM) neural network and sliding window estimation model is proposed and applied in practical industrial scenarios. This method uses the operating data of the motor drive device in the actual industrial application scenario as the training and test data set and uses the external operating parameters of the IGBT module to estimate the junction temperature of the IGBT module. Compared with the internal operating parameters of the IGBT module based on switching transient, the external operating parameters are easier to collect and process, and more suitable for practical application scenes. A sliding window estimation model is proposed to estimate the junction temperature of the IGBT module. Compared with the point‐to‐point estimation method, the sliding window estimation method can capture the influence of historical operation data better and has a higher capability of time series data estimation. The IGBT junction temperature estimation of sliding windows is realized by the LSTM neural network, which is more suitable for time series estimation in real industrial scenarios. The experimental results show that the estimation accuracy of the sliding window estimation method is better than that of the point‐to‐point estimation method, and the accuracy of the sliding window estimation method based on LSTM is better than that of the sliding window estimation method based on other machine learning models. It proves that the proposed method can better capture the dynamic process of the system and has higher estimation accuracy.

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Background radiation compensation calibration method for film cooling infrared temperature measurement based on BP neural network

Li,

Liu,

Meng

et al. 2024

International Communications in Heat and Mass Transfer

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BP neural network for non-invasive IGBT junction temperature online detection

Cited by 12 publications

References 18 publications

Research on the Application of Gray Prediction and BP Neural Network in English-Chinese Translation Quality Assessment

Research on the Application of Gray Prediction and BP Neural Network in English-Chinese Translation Quality Assessment

IGBT Junction Temperature Estimation Based on External Parameters of Multiple Drive Devices in Practical Industrial Scenario

Background radiation compensation calibration method for film cooling infrared temperature measurement based on BP neural network

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