As mission-critical components in aircraft flap actuation systems, aerospace self-lubricating bearings play a pivotal role in ensuring operational safety during critical flight phases. Conventional machine learning approaches for remaining useful life (RUL) prediction suffer from limited time-series and nonlinear modelling capabilities. Especially individual models, built on a single algorithmic paradigm, struggle to address the complex, multi-dimensional challenges of RUL prediction. In order to solve the above challenge, this study proposes an innovative hybrid deep learning method integrating modernized temporal convolutional Networks (ModernTCN) with attention-enhanced Transformer (MTCAT) for RUL prediction. First, ModernTCN employs optimized dilated convolutions to capture multi-scale local temporal patterns efficiently. Second, an attention-enhanced Transformer leverages multi-head self-attention to model long-range dependencies and global degradation trends. Finally, the MTCAT is validated on the custom test rig, replicating real-world conditions, where it achieves a 30-50% reduction in root mean square error (RMSE) compared to baseline models. By autonomously learning hierarchical features from raw data, the MTCAT model minimizes manual feature engineering, demonstrating robustness and scalability for RUL prediction.

Pallet automation systems (PASs) are critical in flexible manufacturing for their ability to integrate diverse production resources, such as machines, fixture pallets (FPs), and setup stations (STs). In traditional flexible manufacturing systems (FMSs), loading/unloading typically occurs at machines, limiting the machines’ capacity. To address this, in PASs, workpieces are loaded and unloaded from the machines at a limited number of STs, so loading, processing, and unloading are three separate segments. However, the research on PASs is limited, and existing studies mainly focused on machines while overlooking FPs and STs. A critical challenge is the tight coupling between resource selection and operation sequencing. To address this gap, we investigate a multiresource-constrained flexible job shop scheduling problem (MRFJSP) with FPs and STs under PASs (MRFFS). First, a mixed-integer programming model is proposed to minimize makespan. Second, a four-layer encoding scheme and a new decoding method with time period insertion based on the intersection of available time of multiple resources (TPI-IARs) are presented to obtain feasible schedule solutions and shrink the search space. Third, a new search algorithm based on critical paths and points mutation (SACP) is developed to effectively balance exploration and exploitation. Finally, four case studies are designed to demonstrate the validity and effectiveness of this work.