Uncertainty quantification in multiaxial fatigue life prediction using Bayesian neural networks

He, GaoYuan; Zhao, YongXiang; Yan, ChuLiang

doi:10.1016/j.engfracmech.2024.109961

Cited by 4 publications

(1 citation statement)

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“…Compared with deep learning, traditional machine learning has the advantages of relatively less data requirements [18][19][20][21], low computing resource consumption, and generalization capabilities [22][23][24][25]. Among them, the BP (Back Propagation) algorithm [26,27], KNN (K-nearest neighbor) algorithm [28] and XGBOOST algorithm [29][30][31] are popular machine learning algorithms used for different purposes. First, the BP algorithm is an artificial neural network algorithm commonly used for supervised learning tasks such as classification and regression.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Approach for Failure and Life Prediction of Electronic Components under Accelerated Life Stress

Qiu,

2024

Electronics

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Researchers worldwide have been focusing on accurately predicting the remaining useful life of electronic devices to ensure reliability in various industries. This has been made possible by advancements in artificial intelligence (AI), machine learning, and Internet of Things (IoT) technologies. However, accurately forecasting device life with minimal data sets, especially in industrial applications, remains a challenge. This paper aims to address this challenge by utilizing machine learning algorithms, specifically BP, XGBOOST, and KNN, to predict device reliability with limited data. The remaining life dataset of electronic components is obtained through simulation for training and testing the algorithms, and the experimental results show that the algorithms achieve a certain level of accuracy, with the error rates being as follows: BP algorithm, 0.01–0.02%; XGBOOST algorithm, 0.01–0.02%; and KNN algorithm, 0–0.07%. By benchmarking these algorithms, the study demonstrates the feasibility of deploying machine learning models for device life prediction with acceptable accuracy loss, and highlights the potential of AI algorithms in predicting the reliability of electronic devices.

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