Dynamic stress response and fatigue life of cantilever beam under non-Gaussian base excitation

The stress response of cantilever beam to non-Gaussian random base excitation is investigated based on Monte-Carlo simulation. First, the statistical properties and spectral characteristics of non-Gaussian random vibrations are analyzed qualitatively; and the conclusion is that spectral method based on power spectrum density (PSD) is not applicable for non-Gaussian random vibrations. Second, the stress response formula of cantilever beam under non-Gaussian random base excitations is established in the time-domain, and the factors influencing the output kurtosis are subsequently determined. Two numerical examples representing different practical situations are analyzed in detail. The discrepancies of the stress responses to Gaussian, steady non-Gaussian and burst non-Gaussian base excitations are analyzed in terms of root mean square (RMS), kurtosis and fatigue damage. The transmissibility of RMS and high-kurtosis of steady non-Gaussian random base excitation is different from that of burst non-Gaussian case. Finally, the fatigue life corresponding to every base excitation is calculated using the rainflow method in conjunction with the Palmgren-Miner rule. Finite element analysis is also carried out for validation. The predicted fatigue lives corresponding to Gaussian, steady non-Gaussian and burst non-Gaussian base excitations are compared quantitatively. Finally, in the fatigue damage point of view, the discrepancies among the three kinds of random base excitations are summarized

A spectral method to estimate fatigue life under broadband non-Gaussian random vibration loading

The aim of this study is to propose a spectral method for assessing the fatigue lives of mechanical components under non-Gaussian random vibration loadings. Efforts are made to extend the Dirlik’s method to non-Gaussian vibration field by introducing the Gaussian mixture model. A symmetric non-Gaussian random vibration can be decomposed into a series of Gaussian components through Gaussian mixture model. Then the rainflow cycle distributions of the Gaussian components can be obtained using Dirlik’s method. The cycle distribution of the underlying non-Gaussian process is derived by compounding the distributions of Gaussian components together. The non-Gaussian cycle distribution, combined with Palmgren-Miner rule is used to predict the fatigue lives of specimens. Comparisons among the proposed method, Dirlik’s solution, nonlinear model in literature, and the experimental data, are carried out extensively. The results have confirmed good accuracy of the proposed method

Reliability Evaluation and Prediction Method with Small Samples

How to accurately evaluate and predict the degradation state of the components with small samples is a critical and practical problem. To address the problems of unknown degradation state of components, difficulty in obtaining relevant environmental data and small sample size in the field of reliability prediction, a reliability evaluation and prediction method based on Cox model and 1D CNN-BiLSTM model is proposed in this paper. Taking the historical fault data of six components of a typical load-haul-dump (LHD) machine as an example, a reliability evaluation method based on Cox model with small sample size is applied by comparing the reliability evaluation models such as logistic regression (LR) model, support vector machine (SVM) model and back propagation neural network (BPNN) model in a comprehensive manner. On this basis, a reliability prediction method based on one-dimensional convolutional neural network-bi-directional long and short-term memory network (1D CNN-BiLSTM) is applied with the objective of minimizing the prediction error. The applicability as well as the effectiveness of the proposed model is verified by comparing typical time series prediction models such as the autoregressive integrated moving average (ARIMA) model and multiple linear regression (MLR). The experimental results show that the proposed model is valuable for the development of reliability plans and for the implementation of reliability maintenance activities