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.

To address the challenges of fully characterizing wheel information and accurately locating and quantifying wheel damage using trackside signals, this paper proposes a multi-sensor data fusion algorithm combined with an improved convolutional neural network (CNN) for wheel tread defect identification. A vehicle-track dynamics coupling model is established based on multi-body dynamics and finite element theory. By strategically arranging fewer sensors, multimodal features are extracted, and data fusion algorithms are optimized for parameters like wheel geometry and vehicle speed. An improved CNN model is then proposed, building upon both 1D-CNN and 2D-CNN architectures. Simutaneously, frequency domain features and image features are fused, leading to a new CNN algorithm model that incorporates these fusion features. Defect feature extraction is performed on the reconstructed signal, and the improved CNN, leveraging the fused data features, is used to achieve wheel damage identification. The effectiveness of the proposed method is validated using both simulation data and actual case studies, in conjunction with a proportional vehicle test rig. The damage identification performance of the proposed model is compared against CNN, BP neural network (BPNN), and support vector machine (SVM) under various signal test sets and data features. Results indicate that the proposed damage identification model can more effectively identify wheel tread defects, showing good consistency with measured results. Fusing data features from different dimensions can characterize defects under varying degrees of damage and significantly improve identification performance. This approach successfully addresses issues where trackside data alone cannot fully reconstruct wheel status, thereby providing crucial technical support for the online damage identification of wheel defects.

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.

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.

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.

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.

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.

Vibration and temperature signals from bearings contain rich fault characteristic information, making them crucial for condition monitoring and fault diagnosis of traction motor bearings in heavy-haul locomotives. This study establishes a thermo-vibration coupling dynamics model for heavy-haul locomotive traction motor bearings, based on vehicle-track coupled dynamics. The model considers the nonlinear normal contact and tangential friction effects between the roller, raceway, as well as their defect areas. The influence of raceway defects in motor bearings on the thermo-vibration coupling characteristics of the traction motor is investigated. Additionally, the mapping relationship between defect width and both vibration response and bearing temperature rise is constructed. Results indicate that when the defect width reaches 1 mm, distinct fault characteristic frequencies appear in the vibration signal spectrum. The root mean square frequency, a frequency-domain statistical indicator, shows an increasing trend across the entire defect width range, while the frequency standard deviation is more sensitive to early defects. Time-domain statistical indicators of vibration signals, such as root mean square (RMS) and kurtosis values, are relatively sensitive to outer raceway defects. Conversely, inner raceway defects lead to a rapid temperature increase in the traction motor bearing, which is prone to triggering temperature rise alarms.

Under the background of industrial big data and intelligent manufacturing, data-driven intelligent fault diagnosis technology has become a crucial enabling technology. It ensures the safe and reliable operation of high-end equipment, facilitates equipment health management, and supports intelligent operation and maintenance. Existing intelligent fault diagnosis models often fail to simultaneously achieve superior diagnostic accuracy, strong noise immunity, high computational efficiency, and robust hyperparameter performance. To address these limitations, this paper proposes a novel spectral ensemble sparse representation classification model-driven super-robust intelligent diagnostic method. The proposed method designs a vibration data augmentation strategy based on cascade segmentation operators, aiming to enhance both the quantity and quality of vibration data samples. It utilizes the spectral features of vibration signals for dictionary atom design and constructs a spectral ensemble dictionary design strategy that incorporates spectral feature fusion. This improves the reconstruction capability of the spectral sparse representation dictionary. The method develops an intelligent recognition strategy based on the spectral sparse approximation error minimization criterion to achieve intelligent diagnosis of test samples health status. The proposed method is validated on a planetary gear transmission fault dataset. Results demonstrate that the intelligent diagnosis method can integrate the advantages of superior diagnostic accuracy, strong noise immunity, high computational efficiency, and robust hyperparameter selection. Its diagnosis results surpass existing advanced methods, showcasing significant application for data-driven intelligent fault diagnosis of industrial equipment.

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.