Journal of Vibroengineering

Journal of Vibroengineering, (in Press).Xufei Wang, Zhiqiang Xie, Ningchao Zhang, Jinde SongThe membrane air spring possesses the advantage of variable vertical stiffness. However, under limited vertical installation space, further investigation is required to analyze how variations in the rubber airbag cord parameters influence the vertical stiffness of membrane air springs. A finite element model of a specific membrane air spring was developed. Under a compression displacement of 40 mm, the effects of parameters such as the number of rubber airbag cord layers, cord spacing, cord angle, and cord cross-sectional area on vertical stiffness were examined. The results indicate that vertical stiffness decreases with an increase in cord spacing or cord angle, but increases with greater cord cross-sectional area or number of cord layers. Subsequently, the orthogonal experimental method was applied to construct an L9 (34) orthogonal table, and the influence of the four cord parameters on the vertical stiffness of the membrane air spring under 40 mm compression displacement was analyzed. The findings revealed that vertical stiffness is least affected by cord angle and most affected by the number of cord layers. The variation trend of vertical stiffness was determined by fitting the optimal parameter combination. Finally, the combination of four cord parameters yielding the maximum vertical stiffness was evaluated when the compression displacement increased to 50 mm, providing a reference for air spring configuration design in confined spaces.

Journal of Vibroengineering, (in Press).Zhihui Yu, Chuan Lin, Chaohui Huang, Yifan Huang, Jiaman LuoVibration signals from high-voltage circuit breakers (HVCB) typically contain complex background noise, and traditional fault diagnosis methods often neglect the temporal relationship between vibration signals and fault characteristics. To address these issues, an IMCEEMDAN-TSG fault diagnosis model based on vibration signals is proposed. First, Pearson correlation coefficient filtering is combined to improve the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (IMCEEMDAN) for adaptive multi-resolution analysis, which effectively separates the intrinsic mode function (IMF), thereby filtering out noise contained in the signal, suppressing mode aliasing, and preserving key signal features. Secondly, a TSG hybrid algorithm is constructed by combining the Temporal Convolutional Network (TCN) embedded with the Self-Attention Mechanism (SAM) and the Gated Recurrent Unit (GRU). This architecture facilitates the parallel feature extraction of multi-channel IMF and the capture of temporal relationships, thereby deeply modeling temporal dependencies and revealing the dynamic evolution of vibration signals. Experimental results demonstrate that the proposed model achieved a fault diagnosis accuracy of 100 % on the HVCB simulation datasets, surpassing the traditional Convolutional Neural Network (CNN) by 19.07 %. Furthermore, compared with conventional algorithms, significant improvements were observed across all classification metrics, providing an accurate and reliable solution for the mechanical fault diagnosis of HVCB.

Journal of Vibroengineering, (in Press).Xiao Meng, Mei WangReliable operation of main mine ventilation fans is essential for mine safety and production continuity. In many mines, fan condition is still judged against fixed vibration limits that do not account for changes in airflow and pressure, which can lead to frequent false alarms and ambiguous interpretation of mechanical degradation. This paper proposes a maintenance-event-constrained vibration health index (MEC-HI) that combines operating-condition modelling with long-term residual vibration analysis using SCADA-level measurements. A linear regression model is first fitted to relate bearing RMS vibration velocity to airflow, differential pressure and motor electrical quantities during confirmed healthy operating phases. The model is then used to estimate the expected vibration level and to compute condition-normalised residual vibration. Positive residuals exceeding a statistically derived tolerance are smoothed and accumulated over time within segments separated by major maintenance events, and the cumulative index is reset after bearing replacement. Unlike many health-indicator studies that rely on high-frequency waveforms or fault-specific feature engineering, the proposed framework targets practical deployment when only routine RMS and operating tags are archived. The approach is demonstrated using a three-year dataset (24,672 operating hours) from an axial-flow main fan in a large underground copper mine. The case study shows that MEC-HI captures the onset and progression of bearing degradation more clearly than raw RMS trends and suppresses load-driven false alarms, while remaining implementable with routinely available SCADA measurements. The framework can be extended to other ventilation fans and rotating machinery operating under strongly varying loads.

Journal of Vibroengineering, (in Press).Xuan Lyu, Renfeng Zhang, Shuo Feng, Xinqiang LiThis paper proposes a novel method for gearbox fault diagnosis, which is capable of identifying both single faults (either in gears or bearings) and various types of compound faults. Vibration signals collected from a test platform were employed to validate the proposed method, where five operating states were configured, including: (1) healthy state; (2) single-tooth breakage of the fixed-axis gear; (3) single-tooth breakage of the planetary gear combined with bearing rolling element damage; (4) planetary gear wear coupled with rolling bearing outer ring damage; and (5) fixed-axis gear root crack, planetary gear wear, and bearing outer ring damage. The proposed method Wilcoxon rank- sum tests and maximum amplitude selection (WTMAS) was used as feature extraction method for vibration signals of different states and to establish the training samples and test samples. The K-Nearest Neighbor (KNN) algorithm was utilized as the classifier for fault type classification and identification. Experimental results demonstrate that the average recognition rate of the proposed method for the five states reaches 95.753 %, indicating that the method exhibits high recognition accuracy for different types of faults and is thus an effective approach for gearbox fault diagnosis.

Journal of Vibroengineering, (in Press).Cabrera Yerry, Velasquez Sergio, Campos Alfredo, Prada Engels, Hernandez PedroThe present study analyzes the causes of increased vibration in Francis-type hydroelectric generators, focusing on the rotor-stator assembly and the rotor support structure (rotor spider), and considering installation and operational performance. Through comprehensive analysis of vibration histories, visual and topographic inspections, roundness measurements, alignment and magnetic-center surveys, and finite element modeling (FEM) under nominal speed and runaway speed conditions (81.8 and 175 rpm), the principal causes of elevated vibration were identified. Key findings include: fatigue fracture of the bases supporting the polar rim support block; loss of rotor-stator magnetic centering (polar rim descent up to 20 mm); and loss of rotor roundness (maximum deviation 1.7 mm). FEM revealed stress concentrations in the original design that exceed the yield strength of A-36 steel and fatigue safety factors below 1 in the critical region. These conditions produce structural imbalance and intermittent vertical forces that increase vibration – particularly during start/stop transients and in pass-through bands of natural frequencies. Bearing issues, the thrust ring flatness and the original alignments/centricities are ruled out as primary causes. The study provides a solid technical basis for corrective interventions and redesign proposals aimed at reducing vibration and improving unit reliability, and constitutes a methodological and practical reference for diagnosing and solving vibration problems in similar hydraulic machines.