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.
A novel composite terminal guidance law with impact angle constraints is proposed for supercavitating torpedoes to intercept maneuvering warships. Based on an adaptive super-twisting algorithm and nonsingular terminal sliding mode (NTSM), the proposed guidance law can guarantee the finite-time convergence of line-of-sight (LOS) angle error and the LOS angular rate error. The new guidance law is a combination of finite-time stability theory, sliding mode control (SMC), tracking differentiator (TD), disturbance observer (DO), and dynamic surface control. A high-order sliding mode TD is used for denoising, tracking, and differentiating the measured target heading angle. A novel DO, with its finite-time stability proved, is designed to estimate the target lateral acceleration for feedforward compensation to attenuate chattering in control input. In the case of a first-order-lag autopilot, a new kind of tracking differentiator is adopted to compute the first-order time derivative of the virtual control command, which can improve the accuracy of dynamic surface control and avoid the “explosion of items” problem encountered with the backstepping control. Finally, numerical simulation results are presented to validate the effectiveness and superiority of the proposed TD, DO, and the composite guidance law.
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