A remaining useful life prediction method of IGBT based on online status data

Zhang, Jinli; Hu, Jinbao; You, Hailong; Jia, Renxu; Wang, Xiaowen; Zhang, Xiaowen

doi:10.1016/j.microrel.2021.114124

Cited by 6 publications

(8 citation statements)

References 12 publications

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“…Thermal stress failure refers to the fracture of the bonding layer or the falling off of the bonding wire of the device caused by thermal stress, which increases the thermal resistance of the chip, and thus causes changes in some characteristic parameters of the device, and this is the main reason for the aging device. Therefore, the current research on the aging failure of IGBT devices mainly refers to the aging failure of devices caused by thermal stress [16]. Unlike electrical stress failure, thermal stress failure is caused by long-time use of the device, which is predictable and can therefore be used for life prediction.…”

Section: Cause Analysis Of Failurementioning

confidence: 99%

Lifetime prediction of IGBT based on empirical wavelet transform combined with long short term memory network model

Li,

Pazilai,

Zhou

2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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The insulated gate bipolar transistor (IGBT) has the process of asymptotic and nonlinear degradation. It is of great significance to analyse its aging process and predict its remaining useful life for the safe and reliable operation of the power system. Therefore, this research introduces a method based on the Empirical Wavelet Transform combined with the Long Short Term Memory Network (EWT-LSTM) method to predict the remaining useful life of IGBT devices, to select the collector-emitter off instantaneous peak voltage as the characteristic signal of IGBT device aging. Firstly, the EWT method is used to decompose the aging signal. Then, in response to the problem of manually setting the number of segmentation layers in the EWT method, the adaptive spectrum segmentation method is used to adaptively determine the number of segmentation layers based on the signal to avoid interference caused by human participation. Finally, the LSTM time series model is used to predict each decomposed signal, and each prediction result is reconstructed to obtain the final prediction result. The results show that EWT-LSTM does not require noise filtering compared to other methods and has better prediction results, enabling better completion of IGBT life prediction work.

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Section: Cause Analysis Of Failurementioning

confidence: 99%

Lifetime prediction of IGBT based on empirical wavelet transform combined with long short term memory network model

Li,

Pazilai,

Zhou

2023

Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023)

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“…The studies [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ] show that the IGBT failure precursor parameters can directly or indirectly reflect the health state of the IGBT, mainly including collector-emitter voltage (V CE ), gate-emitter threshold voltage (V GEth ), collector shutdown current (I C ), gate leakage current (I G ), junction temperature and thermal resistance, etc.…”

Section: Analysis Of Igbt Failure Modes and Their Precursor Parametersmentioning

confidence: 99%

“…Due to the decline in V CE caused by chip bonding and the loss of bonding wires, the increase in V CE value has a high impact, and it is generally believed that the V CE value increases as IGBT ages. The V CE is the most commonly used health monitoring parameter due to its easy detection of V CE through sensors under laboratory conditions [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. The part of the shutdown current I C in the slow falling phase of the IGBT is called the tail current, and the shutdown current duration can be used as a performance parameter to characterize the IGBT latch-up effect, so the tailing current can also be used as an electrical parameter to characterize the IGBT latch-up effect.…”

Section: Analysis Of Igbt Failure Modes and Their Precursor Parametersmentioning

confidence: 99%

“…With the rapid development of artificial intelligence algorithms, many methods such as neural networks [ 9 , 10 , 11 ] and support vector machines [ 12 ] are used to build the prediction methods. The data-driven method based on fault precursor parameters [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ] is built on parameters that can characterize the aging of IGBTs. This method determines the current operation status and predicts the future degradation of fault precursor parameters based on the collected data under historical operating conditions.…”

Section: Introductionmentioning

confidence: 99%

“…Lu and Christou also extracted the performance degradation indicator from the collector-emitter voltage, and used the Long Short-Term Memory (LSTM) prediction model to predict the performance degradation trend of IGBT, in which the average absolute error can be predicted as low as 0.0322 [ 14 ]. Zhang [ 15 , 16 ] et al extracted parameters such as saturation voltage drop and tailing current for fusion, introduced a comprehensive health indicator, and obtained IGBT health status assessment results. Liu et al introduced a CNN1D-LSTM hybrid model to construct the IGBT life prediction model with respect to collector emitter voltage, which characterizes IGBT failure modes of weld layer failure and latch failure in [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performance Degradation Modeling and Its Prediction Algorithm of an IGBT Gate Oxide Layer Based on a CNN-LSTM Network

Wang

Zhou

et al. 2023

Micromachines

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The problem of health status prediction of insulated-gate bipolar transistors (IGBTs) has gained significant attention in the field of health management of power electronic equipment. The performance degradation of the IGBT gate oxide layer is one of the most important failure modes. In view of failure mechanism analysis and the easy implementation of monitoring circuits, this paper selects the gate leakage current of an IGBT as the precursor parameter of gate oxide degradation, and uses time domain characteristic analysis, gray correlation degree, Mahalanobis distance, Kalman filter, and other methods to carry out feature selection and fusion. Finally, it obtains a health indicator, characterizing the degradation of IGBT gate oxide. The degradation prediction model of the IGBT gate oxide layer is constructed by the Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) Network, which show the highest fitting accuracy compared with Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and CNN-LSTM models in our experiment. The extraction of the health indicator and the construction and verification of the degradation prediction model are carried out on the dataset released by the NASA-Ames Laboratory, and the average absolute error of performance degradation prediction is as low as 0.0216. These results show the feasibility of the gate leakage current as a precursor parameter of IGBT gate oxide layer failure, as well as the accuracy and reliability of the CNN-LSTM prediction model.

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Life prediction of IGBT module for nuclear power plant rod position indicating and rod control system based on SDAE-LSTM

Chen,

Dai,

Liu

et al. 2024

Nuclear Engineering and Technology

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A remaining useful life prediction method of IGBT based on online status data

Cited by 6 publications

References 12 publications

Lifetime prediction of IGBT based on empirical wavelet transform combined with long short term memory network model

Lifetime prediction of IGBT based on empirical wavelet transform combined with long short term memory network model

Performance Degradation Modeling and Its Prediction Algorithm of an IGBT Gate Oxide Layer Based on a CNN-LSTM Network

Life prediction of IGBT module for nuclear power plant rod position indicating and rod control system based on SDAE-LSTM

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