To solve the problem of low accuracy of remaining useful life (RUL) prediction caused by insufficient sample data of equipment under complex operating conditions, an RUL prediction method of small sample equipment based on a deep convolutional neural network—bidirectional long short-term memory network (DCNN-BiLSTM) and domain adaptation is proposed. Firstly, in order to extract the common features of the equipment under the condition of sufficient samples, a network model that combines the deep convolutional neural network (DCNN) and the bidirectional long short-term memory network (BiLSTM) was used to train the source domain and target domain data simultaneously. The Maximum Mean Discrepancy (MMD) was used to constrain the distribution difference and achieve adaptive matching and feature alignment between the target domain samples and the source domain samples. After obtaining the pre-trained model, fine-tuning was used to transfer the network structure and parameters of the pre-trained model to the target domain for training, perform network optimization and finally obtain an RUL prediction model that was more suitable for the target domain data. The method was validated on a simulation dataset of commercial modular aero-propulsion provided by NASA, and the experimental results show that the method improves the prediction accuracy and generalization ability of equipment RUL under cross-working conditions and small sample conditions.

In this study, a novel ultra-broadband absorber is suggested and numerically analyzed to demonstrate that the suggested absorber can attain an average absorbance of 98.6% in the visible to near-infrared wavelength range(496–2100 nm). The structure of the proposed new ultra-wideband absorber consists of five thin films of silicon dioxide (SiO2), iron (Fe), magnesium fluoride (MgF2), chromium (Cr) and aluminium (Al). We have examined the structure’s electromagnetic field intensity distribution at numerous selected optical wavelengths and the influence of various structural parameters on the absorption performance of the absorber to offer a physical mechanism underlying the ultra-broadband absorption effect. Furthermore, in the presence of high-performance absorption, the structure has the effect of stabilizing absorption at large angles of incidence and is polarization independent at vertical angles of incidence. The study also assesses the solar absorption capability of this structure, indicating that the structure has potential applications in solar absorption, like solar energy collection and conversion, solar power generation, and thermal emitters.

In this study, a novel ultra-broadband absorber is suggested and numerically analyzed to demonstrate that the suggested absorber can attain an average absorbance of 98.6% in the visible to near-infrared wavelength range(496–2100 nm). The structure of the proposed new ultra-wideband absorber consists of five thin films of silicon dioxide (SiO2), iron (Fe), magnesium fluoride (MgF2), chromium (Cr) and aluminium (Al). We have examined the structure’s electromagnetic field intensity distribution at numerous selected optical wavelengths and the influence of various structural parameters on the absorption performance of the absorber to offer a physical mechanism underlying the ultra-broadband absorption effect. Furthermore, in the presence of high-performance absorption, the structure has the effect of stabilizing absorption at large angles of incidence and is polarization independent at vertical angles of incidence. The study also assesses the solar absorption capability of this structure, indicating that the structure has potential applications in solar absorption, like solar energy collection and conversion, solar power generation, and thermal emitters.