Remaining useful life (RUL) prediction plays a significant role in developing the condition-based maintenance and improving the reliability and safety of machines. This paper proposes a remaining useful life prediction scheme combining deep-learning-based health indicator and a new relevance vector machine. First, both one-dimensional time-series information and two-dimensional time-frequency maps are input into a hybrid deep-learning structure network consisting of convolutional neural network (CNN) and long short-term memory network (LSTM) to construct health indicator (HI). Then, the prediction results and confidence interval are calculated by a new RVM enhanced by a polynomial regression model. The proposed method is verified by the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed method in improving the prediction accuracy and analyzing the prediction uncertainty.

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In this study, a simple and unified process is established for transient vibration analysis of functionally graded material (FGM) sandwich plates in thermal environment. The temperature field, considered constant in the plane, is distributed along the thickness with uniform, linear and nonlinear profiles. For the material properties, both temperature and position dependence are taken into account. A further refined zigzag plate theory accounting for partitioned transverse displacements and piecewise-continuous in-plane displacements is developed within the framework of Hamilton’s principle including thermal effects. Appropriately and simplicity representation of the deformation states is provided in the governing equations. A spectral analysis technique, namely, method of reverberation ray matrix (MRRM), is employed to calculate the transient vibration responses of FGM sandwich plates with general boundary conditions and arbitrary external loadings. The artificial spring technology and the equivalent wave source vector are introduced to improve the numerical stability and parametric adjustability of MRRM. The accuracy, flexibility and efficiency of the proposed process are discussed using many numerical examples. On this basis, the effects of the boundary parameters, FGM gradient index, core-to-facesheet thickness ratio, thermal properties and external loadings on the transient vibration behaviors of FGM sandwich plates are thoroughly investigated.

The task of remaining useful life (RUL) uncertainty management is the major challenge in solving the failure of the complex mechanical system. Primary research methods use statistical models or stochastic processes to fit the distribution of historical degradation data. However, it is difficult to accurately capture the degradation information of monitoring big data through statistics in practice. In this paper, the prediction interval (PI) obtained by the proposed feature attention-log-norm bidirectional gated recurrent unit (FA-LBiGRU) model is adopted to quantify the prediction uncertainty of RUL. Initially, the critical feature vectors are extracted from multi-dimensional, nonlinear, and large-scale sensor signals using the feature attention mechanism. Additionally, the BiGRU network is used to model and learn the time-varying characteristics of the attention-weighted features from the forward and backward directions, and the network parameters are trained by the maximum log-likelihood loss function. Ultimately, the probability density function based on the lognormal distribution is calculated to measure the uncertainty of the equipment RUL. The effectiveness of the proposed method is verified through the well-known benchmark data set of the turbofan engines provided by NASA. The experimental results show that the proposed methods can obtain higher point prediction accuracy for the complex system compared with state-of-the-art approaches and highquality PIs satisfying real-time requirements.INDEX TERMS fusion model, gated recurrent unit, prediction intervals, remaining useful life, system prognostics, uncertainty management.