Motor current monitoring systems have garnered significant attention due to their non-invasive and cost-effective advantages. However, conventional current spectrum analysis is susceptible to inherent harmonic and installation errors, and the high amplitude of the fundamental frequency can obscure fault characteristics. To reveal the frequency modulation patterns in motor current caused by reduced meshing stiffness due to gear faults, a motor current model incorporating faulty gear meshing stiffness is established, and its instantaneous frequency expression is derived. Addressing the limitation of traditional time-frequency analysis methods, which often suffer from low instantaneous frequency estimation accuracy, this paper proposes an instantaneous frequency polar view method based on high-order synchrosqueezing transform (HSST) for extracting gear fault features. This method intuitively demonstrates gearbox faults by detecting frequency modulation characteristics that are synchronized with the meshing period of faulty teeth. The instantaneous frequency polar view effectively avoids interference from inherent harmonics and fundamental frequency, offering a unique representation of gear fault characteristics. Analysis of motor current signals from an RV gearbox test rig validates the accuracy of the proposed motor current model and the distribution patterns of fault characteristics. It also confirms the effectiveness of the instantaneous frequency polar view method based on HSST for gearbox faults diagnosis.

As a core component of an aero-engine, the structural integrity of a blade directly determines the engine’s performance and flight safety. Under extreme working conditions such as high temperature, high pressure, and high-speed rotation, blades are prone to generating micro-cracks under the action of complex stress fields. Once cracks propagate and cause blade fracture, they will trigger chain damage, posing significant safety hazards. Based on the damage tolerance concept, the critical duration during which a blade can still operate safely after crack initiation is defined as the remaining useful life (RUL).To address this, this study proposes a mechanism-data dual-driven RUL prediction method integrating the Paris crack propagation law and physics-informed neural networks (PINN). By constructing a loss function that incorporates physical constraints, this method regularizes and constrains the gradients of the neural network. It enables inverse identification of crack propagation parameters while effectively improving the model’s prediction accuracy under limited monitoring data. For aero-engine blades and CT (compact tension) specimens, compared with traditional physical models and data-driven methods, the proposed method dynamically updates characteristic parameters to adapt to system changes, significantly reducing prediction errors under limited sample conditions. Additionally, the PINN model developed in this study features lightweight architecture and fast inference capabilities, meeting the requirements of online monitoring and predictive maintenance. This method provides a new technical pathway for health management and intelligent operation and maintenance of aero-engines.

Gear systems in precision machinery and aerospace applications are subjected to complex vibration problems due to mass eccentricity, time-varying backlash, and dynamic meshing parameter variations. A nonlinear dynamic model with six degrees of freedom is established, incorporating time-varying meshing stiffness, derived using the potential energy method and mass eccentricity. The Runge-Kutta method is employed to solve the system response under varying eccentricities and rotational speeds. Time-domain and frequency-domain analyses, phase portraits, and Poincaré maps are used to investigate the dynamic characteristics. The results indicate that mass eccentricity significantly influences system behavior, leading to the evolution from single-period to multi-period motions (e.g., 20-period cycles), and aggravates bifurcation and oscillation phenomena. The findings provide theoretical support for structural optimization and vibration control of gear transmission systems.

In planetary needle roller bearings, slip ratio and raceway surface quality mutually influence each other. The propogation of early localized raceway defects can significantly impact the cage slip and service performance. To address this problem, a slip dynamic model for planetary needle roller bearings is established, specifically considering localized raceway defects. This model is used to analyze the influence of localized defect size on the slip rate of the bearing. Based on the morphological characteristics of raceway localized defects, the displacement and friction coefficient of the rolling element passing through the localized defect area on the raceway surface are represented by piecewise functions. These piecewise displacement excitation and friction coefficient models are then integrated into the slip dynamic model to investigate the effects of localized defect width and depth on the cage slip. Results indicate that when localized defects occur on the inner and outer raceways, the friction coefficients at the corresponding positions significantly increase. The influence of inner and outer raceway localized defects on the cage slip varies considerably. As the localized defect width on both inner and outer raceways increases, the cage slip rises. However, the localized defect depth has little effect on the cage slip.

New fault modes will continuously emerge in the long-term operation and service process of machinery equipment, which poses higher requirement of the continual learning and lifelong diagnosis capability for intelligent diagnostic models. Lifelong intelligent diagnosis technology driven by class-incremental learning provides new approaches to ensure the full lifecycle safe operation of high-end equipment. However, existing class-incremental learning methods cannot address the problem of efficient incremental transfer diagnosis under the circumstance of cross-operating conditions. To this end, this paper proposes a cross-domain lifelong intelligent diagnostic method driven by meta-class-incremental transfer learning. An enhanced feature extractor is developed via integrating deep residual networks with a convolutional block attention feature fusion module to achieve deep feature extraction and fusion across channel and spatial dimensions. A multi-level knowledge distillation strategy is constructed through combining feature-level and decision-level knowledge distillation mechanisms to effectively address catastrophic forgetting issues in incremental transfer diagnostic scenarios. A meta-class-incremental parameter learning mechanism is proposed by innovatively incorporating the idea of meta-learning into class-incremental learning framework, thus improving the model generalization ability for incremental transfer diagnosis. Experiment validations were conducted on subway train transmission system test rig. Results show that the proposed method achieves an average diagnostic accuracy of 94.96% and an average forgetting rate of 3.85% across different incremental transfer diagnostic scenarios, and outperforms state-of-the-art class-incremental learning methods, offering insights for achieving lifelong intelligent fault diagnosis in full lifecycle health management of high-end equipment.

Gear transmission systems are critical for power delivery in major equipment, yet they are susceptible to diverse and unpredictable to diverse and unpredictable faults, compromising operational safety and service reliability. In practical engineering applications, the absence of fault samples and unknown fault locations pose significant challenges. To address these issues, this paper proposes an unsupervised hypersphere-based fault localization (UHFL) method empowered by mode component energy features. This method extracts mode component energy features that are not only related to the fault mechanisms of transmission components but also possess clear fault localization interpretability. These features then enable both unsupervised anomaly detection and interpretable fault localization. Specifically, an unsupervised data description model is constructed using the proposed features. An attribution explanation method is introduced to quantify the contribution of each feature to the anomaly detection result, thereby achieving interpretable fault localization of gear transmission system components under conditions lacking fault samples. The proposed method is validated through single-fault and compound-fault localization experiments conducted on a helicopter main reducer planetary stage test bench and an armored vehicle transmission system test bench. Experimental results demonstrate that the proposed UHFL method can accurately localize faults in transmission components without requiring any fault sample training. This method offers an effective solution for fault localization in gear transmission systems under data-scarce conditions, showcasing valuable engineering promotion potential and application prospects.

Planet bearing kinematics involve spinning-revolution coupling, resulting in complex and weak fault vibration signals that pose a tremendous challenge to fault diagnosis. Under time-varying speed conditions, the frequency characteristics of gear mesh vibrations overlap with those of planet bearing faults, severely interfering with their fault diagnosis. To address this issue, this paper proposes an order-frequency spectral correlation analysis method for non-stationary signals. The method removes the time-varying low-frequency amplitude envelope of the vibration signal and performs angular domain resampling to stabilize the order characteristics of gear components. Discrete random separation in the order domain is applied to eliminate gear vibration while retaining residual random components. These random components are inverse angular domain resampled to restore the original amplitude envelope, and planet bearing fault features are extracted through their order-frequency spectral correlation or coherence. This method enhances planet bearing fault features and improves the diagnosis capability under time-varying speed conditions. The principle of the method is demonstrated through numerical simulation analysis. Its performance is validated experimentally by successfully diagnosing localized fault on the inner and outer race and rolling elements of planet bearings.

Key components of industrial robots are prone to early-stage performance degradation under complex operating conditions, characterized by strongly non-stationary responses and significant heterogeneity across sensing channels. Traditional diagnostic methods struggle with robust and interpretable fusion of multi-source information, limiting their practical deployment. This paper proposes a dual-channel intelligent diagnostic method for robotic transmission mechanisms, integrating physics-driven sensitivity weighting and residual uncertainty compensation (RUC). Specifically, vibration and torque signals, representing structural response and driving excitation respectively, are selected due to their distinct temporal scales and complementary physical characteristics. A three-layer mapping (fault type-dynamic response characteristic-sensing channel) is constructed to quantify channel dominance for different fault modes. Then, a multi-scale sensitivity evaluation mechanism based on signal-to-noise ratio (SNR), modulation index (MI), and kurtosis guides adaptive weight allocation, while the RUC strategy enhances the expression of features from weakly dominant channels, improving fusion stability. Finally, a physically interpretable and lightweight diagnostic framework is established. Experiments conducted on a public gearbox dataset validate that the proposed method provides superior diagnostic accuracy, interpretability, and deployment potential, demonstrating significant promise for physically consistent multi-source fusion diagnosis in robotic transmission systems.

Traditional bearing fault diagnosis methods often suffer from low accuracy and weak model generalization under varying working conditions due to diverse sample distributions, scarcity of fault samples, and limited feature extraction capabilities of some few-shot learning algorithms. To address these challenges, this paper proposes a novel method for variable condition bearing fault diagnosis that combines a squeeze-and-excitation residual network (SE-ResNet) with meta-transfer learning (MTL). One-dimensional bearing vibration signals collected under different working conditions are converted into time-frequency images using continuous wavelet transform (CWT), thereby transforming the bearing fault diagnosis task into an image recognition problem. A squeeze-and-excitation (SE) attention mechanism is introduced to construct an SE-ResNet backbone network model. This focuses on more effective feature channels, thereby enhancing feature extraction and representation capabilities. Leveraging the advantages of transfer learning (which provides robust initial deep network parameters) and meta-learning (which enables rapid adaptation), the model undergoes sequential pre-training and meta-transfer training. This process yields a high-precision meta-transfer network that can be fine-tuned with only a small number of samples, ultimately achieving accurate bearing fault diagnosis under variable working conditions. The proposed method is validated using two benchmark datasets and a bearing fault simulation test bench developed in the laboratory. Comparative analysis with other methods demonstrates that the proposed method exhibits higher recognition accuracy and superior generalization performance for bearing fault diagnosis under both few-shot and variable working conditions.

To effectively detect and localize vibration faults such as clamp loosening in aero engine accessory pipes, this paper proposes a novel diagnosis method based on dynamic responses of accessory pipes and transmissibility functions. The dynamic model and equation of the faulty pipe are established based on the principle of structural similarity and dynamic similarity, where the impact of the fault is simulated as an additional nonlinear load acting on related position of the pipe. Through the analysis of structural dynamic characteristics, the relationship between dynamic responses and fault occurrence and location is derived and analyzed, a fault diagnosis method based on transmissibility function is then proposed, and the operating process of the method is summarized as well. The accuracy and practicality of the proposed fault diagnosis method are verified through multiple experimental examples. Theoretical analysis and testing results show that the diagnostic method proposed in this paper can accurately detect and localize the existence and position of the clamp loosening fault. Meanwhile, this diagnostic method is suitable for single and multiple clamp loosening faults in aero engine accessory pipes.