Operational safety and reliability have become increasingly paramount in large and complex rotating machinery in industrial manufacturing. Recent strides in data-driven fault diagnosis have significantly advanced the fields of machinery health assessment, fault identification, and prediction. This review delves into the existing literature on fault diagnosis technology for rotating machinery, categorizing the various diagnostic approaches. It further examines the evolution of fault diagnosis technology based on dimensionless indices and elucidates the five latest progressions of foundational theories: the design and optimization of dimensionless indices, extraction and features of mutual dimensionless indices, fault diagnosis technology based on dimensionless indices and artificial immune system, fusion technology of dimensionless indices in time and frequency domains, and datasets. The applications of fault diagnosis technology based on dimensionless indices in the petrochemical industry, high-speed railway, shipping and aerospace are discussed. Future directions for research are outlined across six key areas: more excellent dimensionless indices, rapid and efficient detectors, fault analysis for small-sample fault events, theoretical systems of time–frequency domain fusion diagnosis, fault diagnosis with standardized test database, and expert knowledge base. This comprehensive review bridges the research gap by systematically examining the roles and applications of dimensionless indices in the fault diagnosis of rotating machinery.

Different from single coal seam mining, the stress evolution in the end mining stage of close‐distance coal seams is extremely complex. The unreasonable position and support of the lower retracement channel will cause serious deformation and damage to the surrounding rock. Taking the Yanzishan coal mine as the engineering background, the deformation and failure characteristics of the retracement channel under different overlay environments and the key influencing factors of position design were discussed by numerical simulation, theoretical analysis, and field investigation. The results show that the coupling superposition of upper coal pillar high stress and mining dynamic pressure will form a dangerous area with severe ground pressure behavior. The retracement channel should be preferentially designed in zone A (overlying mining roadways), followed by zone B (overlying end‐mining coal pillar), and finally, zone C (overlying section coal pillar). In addition, the rational horizontal distance between the lower retracement channel and the upper end‐mining coal pillar should make the channel in a good stress environment. The safety distance between the retracement channel and the nearest main roadway (end‐mining coal pillar width) should be greater than the severe range of advance abutment pressure. Finally, the design principle and control strategy for the lower retracement channel is proposed. The feasibility and rationality of the study are verified by industrial applications.

A comprehensive understanding of airflow temperature distribution within high-temperature tunnels is crucial for developing effective cooling strategies that ensure a safe environment and acceptable construction costs. In this paper, we introduce a novel cooling strategy that integrates thermal insulation layers and heat exchangers aligned along the tunnel axis (TIL-HE strategy). We investigate variations in airflow temperature and valid ventilation distance (VVD) and compare them with two other cooling strategies: natural tunnels only employing mechanical ventilation (NT strategy) and tunnels featuring thermal insulation layers (TIL strategy), through the 3D k-ε turbulence model in COMSOL Multiphysics. Our findings indicate that (1) the TIL-HE strategy demonstrates superior cooling performance, resulting in significantly lower airflow temperatures and markedly higher VVD; (2) higher water velocity and more heat exchangers contribute to lower airflow temperature and prolonged VVD; (3) positioning the heat exchangers within the surrounding rock rather than inside the insulation layer leads to even lower airflow temperature and longer VVD. Longitudinal-arranged heat exchangers present fewer construction challenges compared to traditional radial-drilled ones, ultimately reducing tunnel construction costs. These findings provide valuable insights for optimizing cooling strategies and engineering parameters in high-temperature tunnel environments.

The deployment of deep learning-based diagnostic models in noisy industrial settings is garnering significant attention, driven by the exigent demand for robust data-driven solutions. However, the efficacy of existing methodologies is impeded by two primary challenges: (1) the suboptimal utilization of vibration signals leads to incomplete extraction of fault features, thereby constraining diagnostic accuracy, and (2) the pervasive interference of environmental noise substantially compromises diagnostic reliability. To address these limitations, this paper introduces a novel time–frequency multi-feature adaptive fusion network (TFMAFNet). The proposed network is distinguished by three core innovations. First, a multi-scale adaptive feature extraction module (MSAFEM) is developed to dynamically capture multi-scale information from time–frequency representations, thus enhancing noise immunity. Second, a separable convolutional attention module is constructed to enable the model to effectively focus on and exploit discriminative time–frequency features amidst significant noise. Third, the time–frequency adaptive fusion module aims to integrate low-level time domain and frequency domain positional details into multi-scale high-level semantic information, feeding it into the attention mechanism to suppress noise and optimize the final feature representation. Extensive experiments on the Case Western Reserve University and Paderborn University datasets demonstrate that TFMAFNet substantially outperforms state-of-the-art models, particularly under strong Gaussian white noise, achieving a remarkable enhancement in diagnostic accuracy.