The work focuses on the post- buckling behavior of functionally graded graphene platelet (FG-GPL)-reinforced porous thick rings with open-cell internal cavities under a uniform external pressure. The generalized rule of mixture and the modified Halpin–Tsai model are here used to evaluate the effective mechanical properties of the ring. Three types of porosity patterns are assumed together with five different GPL distributions as reinforcement across the ring thickness. The theoretical formulation relies on a 2D-plane stress linear elasticity theory and Green strain field in conjunction a virtual work principle to derive the nonlinear governing equations of the post-buckling problem. Unlike the simple ring models, 2D elasticity considers the thickness stretching. The finite element model combined with an iterative Newton–Raphson algorithm is used to obtain the post-buckling path of the ring up to the collapse. A systematic investigation evaluates the effect of the weight fraction of nanofillers, the coefficient of porosity, porosity distribution, and the GPLs distribution on the deep post-buckling path of the ring. Based on the results, it is found that the buckling value and post-buckling strength increase considerably (by approximately 80%) by increasing the weight fraction of the nanofiller of about 1%.

With the rapid development of new energy vehicle technologies, higher demands have been placed on fault diagnosis for automotive transmission gearboxes. To address the poor adaptability of traditional methods under complex operating conditions, this paper proposes a sensor data-driven fault diagnosis method based on improved ensemble empirical mode decomposition (EEMD) combined with convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The method incorporates a dynamic noise adjustment mechanism, allowing the noise amplitude to adapt to the characteristics of the signal. This improves the stability and accuracy of signal decomposition, effectively reducing the instability and error accumulation associated with fixed-amplitude white noise in traditional EEMD. By combining the CNN and BiLSTM modules, the approach achieves efficient feature extraction and dynamic modeling. First, vibration signals of the transmission gearbox under different operating states are collected via sensors, and an improved EEMD method is employed to decompose the signals, removing background noise and nonstationary components to extract diagnostically significant intrinsic mode functions (IMFs). Then, the CNN is utilized to extract features from the IMFs, deeply mining their spatiotemporal characteristics, while the BiLSTM captures the temporal sequence dependencies of the signals, enhancing the comprehensive modeling of nonlinear and dynamic fault features. The combination of these two networks enables efficient adaptation to complex conditions, achieving accurate classification and identification of multiple gearbox fault modes. Results indicate that the proposed approach is highly accurate and robust for identifying gearbox fault modes, significantly exceeding the performance of conventional methods and isolated network models. This provides an efficient and intelligent solution for fault diagnosis of automotive transmission gearboxes.