Li-Qun Fu

Accurate Remaining Useful Life (RUL) prediction is vital for industrial reliability. While deep learning methods like Temporal Convolutional Networks (TCN) have shown potential, they suffer from static convolutional kernels that fail to adaptively capture complex degradation dynamics and often ignore frequency-domain physical priors. To address these limitations, we propose a Temporal-Physics Aware Dynamic Network (TPADN). TPADN adopts a Deep-stage Decomposition Strategy that splits deep stages into an Overview-Net for global degradation context generation and a Focus-Net with one-dimensional Context-Mixing Dynamic Convolution, enabling input-dependent causal kernel generation. A physics-aware input strategy fuses raw vibration signals with their Envelope Spectrum to inject fault characteristic frequency information, while a learnable Soft Thresholding mechanism suppresses noise adaptively. Experiments on FEMTO and XJTU-SY datasets demonstrate that TPADN achieves superior accuracy and robustness compared to state-of-the-art methods.

Photovoltaic (PV) power forecasting can provide strong support for the safe operation of the power system. Existing forecasting methods are ineffective for grid scheduling decisions or risk analysis. The novel multicluster interval prediction method is proposed to consider the volatility and randomness of PV power output. First, this method utilizes the sparse autoencoder (SAE) and Bayesian regularized NARX network (BRNARX) for point forecasting of PV power. Second, density peak clustering improved by kernel Mahalanobis distance (KMDDPC) is applied to classify the dataset into multiple clusters, including forecasting error and meteorological factors. Finally, the joint probability density is established by multivariate kernel density estimation (MKDE) to accomplish the PV power interval prediction. The proposed hybrid method is applied for the interval prediction of PV power at Yulara, Australia. Comparative research of point forecasting is implemented to evaluate the machine learning and deep learning methods, with the proposed SAE‐BRNARX under four different periods. Results shows that the average values of nRMSE, MRE, nMAE, and R² for the four periods are 4.45%, 0.90%, −0.15%, 3.39%, and 95.93%, respectively. Moreover, the results of interval prediction obtained by the other interval prediction approaches are compared with the proposed KMDDPC‐MKDE. It shows that the average values of PICP, PINAW, ACE, and nMPICD for four periods are 93.93%, 9.50%, 3.93%, and 7.10% at 90% confidence level, respectively. Outcomes demonstrate that the proposed method can obtain more accuracy, a higher coverage rate, narrower average bandwidth, and a closer distance between the middle of interval and actual value than other methods.