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Abstract
IGBTs are used everywhere ranging from aerospace, to transportation systems to the grid but it’s the most fragile device in power electronics. So it’s very critical to evaluate the health state and take advanced and active maintenance measures to avoid the accidents. This paper develops a rule-based sub-safety recognition model using neural networks to evaluate the degradation degree of the IGBTs and determine the health state. The model was validated with two groups of experimental data.
Highlights
The safety and reliability of IGBTs are highly required in transportation fields, aerospace and so on
A health indicator is proposed based on the minimum quantization error (MQE) and the degradation degree can be determined by the output of self-organizing map neural network (SOMNN)
For IGBT sub-safety recognition, 33 IGBTs were used in the power cycling test; 11 healthy IGBTs were used as healthy reference and the remaining 22 IGBTs were used for the accelerated power cycling test
Summary
The safety and reliability of IGBTs are highly required in transportation fields, aerospace and so on. The data-driven approaches mainly use artificial intelligent techniques such as neural networks, Bayesian networks, Markov process, or statistical methods to learn the degradation phenomenon [5] and to predict the future health state of the given systems. Most of the research has focused on anomaly detection RUL prediction of the IGBTs. few papers have conducted research on sub-safety recognition of the IGBTs, defined as the IGBT’s early health state of its failure, even if it is significant to keep the. The self-organizing map neural network (SOMNN) [12], a type of artificial neural network that is trained using unsupervised learning to produce a two-dimensional discretized representation of the input space of the training samples, is used as a black-box model to learn the IGBT’s degradation behavior and segregate the measurement data in-situ into possible health conditions (i.e., safety, sub-safety, and failure).
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