Rotating machinery is a key component in modern industry, and its operating condition directly affects equipment safety and production reliability. However, discrepancies among different machines cause source–target distribution shifts, while fault annotation for target machines is costly, limiting the performance of deep learning-based diagnosis under cross-machine scenarios with limited labels. To address these issues, this paper proposes a multi-scale time–frequency semantic consistency model based on self-supervised transferable representation learning, termed MTFSC. First, augmented waveform views and multi-scale frequency-domain views are constructed from unlabeled source-domain vibration signals for self-supervised pre-training without source labels. Then, a time-domain impulse-aware feature extractor and a time–frequency decoupled spectral feature extractor are designed to enhance local impulsive responses and emphasize fault-sensitive time–frequency patterns. Furthermore, a semantic-aware soft contrastive loss is developed to mine potential semantic neighbors from multi-scale frequency-domain structural similarity, reducing false-negative effects in conventional hard-label contrastive learning. Finally, the pre-trained time-domain extractor is transferred to the target machine and fine-tuned with limited labeled samples. Experimental results show that MTFSC outperforms comparison methods under different labeled sample ratios and achieves an average accuracy of 97.5% across four cross-machine diagnostic tasks.

A common engineering challenge is that a single sound-absorbing material cannot adequately cover the full frequency range in broadband noise control of commercial vehicle cabs; with the aim of addressing this challenge, this study employs a synergistic noise reduction method integrating hybrid finite element–statistical energy analysis (FE–SEA) with foam mixture design. A hybrid FE–SEA cab model is first established, and panel contribution analysis is used to identify key radiating areas such as the floor and rear panel, as well as their corresponding energy-concentrated frequency bands. High- and low-flow-resistance polyurethane foams and melamine foam are selected as base materials. A simplex-centroid mixture design is adopted to construct a Scheffé regression model using the average noise reduction contribution in frequency bands as the response, and Derringer–Suich multi-response optimization is applied to obtain the optimal proportions. Full-vehicle simulation results show that the optimized formulation achieves noise reduction contributions of 13.87, 16.42, and 10.84 dB in the 200–500 Hz, 800–1600 Hz, and 2000–3150 Hz bands, respectively, without evident weak-band performance, reducing the overall SPL to 68.25 dB(A). The proposed method establishes a bidirectional linkage between vehicle acoustic response and material formulation, providing a reproducible framework for the forward design and broadband acoustic optimization of commercial vehicle acoustic packages.