International Journal of Prognostics and Health Management

Uncertainty Assessment Framework for IGBT Lifetime Models. A Case Study of Solder-Free Modules

2024-11-12T12:54:54+00:00

Insulated gate bipolar transistors (IGBTs) are ubiquitous semiconductor devices used in diverse electronic power applications. The reliability and lifetime assessment of IGBTs is intricate and influenced by different ageing processes. One of the main ageing mechanisms is the bond wire lift-off failure mode. The model used to describe this failure mode and estimate the IGBT lifetime is influenced by different variables and factors, which are stochastic, and tend to be specifically adjusted for different IGBT modules and applications. However, unless these variables are not assessed with respect to potential sources of uncertainty, the IGBT lifetime estimate leads to a single-value deterministic estimate, which, frequently, results inaccurate. In this context, assessing the influence of the variability of these variables on the lifetime model is a crucial activity for an uncertainty-aware IGBT lifetime estimate and adoption of appropriate sensing technology. Accordingly, this paper presents a methodology to evaluate the impact of the uncertainty of IGBT lifetime parameters on the lifetime estimate. The approach is first validated on three different experimental IGBT operation profiles, demonstrating the impact of variations of certain variables on the damage estimation. The approach has been tested here for a single lifetime model, but it is generally applicable to other IGBT lifetime models.

Adaptive Res-LSTM Attention-based Remaining Useful Lifetime Prognosis of Rolling Bearings

2024-10-06T17:38:28+00:00

Predicting the Remaining Useful Lifetime (RUL) of bearings is crucial for the maintenance and reliability of rotating machinery. This paper presents a novel approach utilizing PRONOSTIA and XJTU-SY datasets for RUL prediction. The proposed methodology leverages Synchrosqueezing Wavelet Transform (SSWT) and Random Projection (RP) to extract significant features from vibration signals. These features are then fed into a Residual Network (ResNet) combined with a temporal attention layer, followed by a Long Short-Term Memory (LSTM) model, referred to as the Adaptive Residual Attention LSTM (ARAL), to assess the Health Indicator (HI) of the bearings. Notably, an exponential data labeling technique is employed instead of traditional linear labeling, enhancing the robustness of the HI assessment. Following the HI estimation, the three-sigma method is applied to identify the degradation starting point. Subsequently, Gaussian Process Regression (GPR) is utilized to predict the RUL from this point forward. The proposed method demonstrates superior performance compared to existing techniques, providing more accurate and reliable RUL predictions. Experimental results show that this integrated approach effectively captures the complex degradation patterns of bearings, making it a valuable tool for prognostics and health management in industrial applications.