Laser powder bed fusion (LPBF) technology, a cutting-edge process in metal additive manufacturing, has been successfully applied in high-end manufacturing sectors like aerospace. However, strong multi-physical field coupling effects frequently lead to dynamic instability in the molten pool, causing widespread porosity defects within fabricated parts and severely impacting forming quality stability. Traditional monitoring methods face limitations such as high cost and deployment difficulties, struggling to meet industrial production demands. To address these challenges, this paper proposes an online monitoring and intelligent internal quality discrimination method based on acoustic emission (AE)-deep learning fusion. An AE sensor-based online monitoring system for the LPBF process was developed. By continously monitoring AE signals throughout the entire process, the mapping relationship between AE signal characteristics and forming quality was revealed, creating a molten pool AE dataset comprising over 80,000 samples. To tackle the difficulty of extracting weak fluctuation features from the molten pool, a frequency domain feature extraction network based on the adaptive Fourier neural operator (AFNO) and a high-dimensional feature mapping classifier based on the Kolmogorov-Arnold network (KAN) were constructed. This approach analyzes molten pool dynamic characteristics through a multi-scale time domain feature fusion mechanism. By precisely mapping high-dimensional features using high-dimensional manifolds, the method achieves enhanced characterization of weak fluctuation features in AE signals and high-precision quality discrimination. Experimental results demonstrate that developed monitoring system effectively captures the dynamic behavior of the molten pool, and the proposed method achieves a quality discrimination accuracy exceeding 97%.

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.

Maximum correlated kurtosis deconvolution (MCKD), which uses correlated kurtosis as its deconvolution target, effectively extracts both periodic and impulsive features of mechanical faults. This is a widely used method for solving rolling bearing fault diagnosis problems. However, the performance of MCKD heavily relies on accurate prior fault period information. Existing solution often only focus on period estimation during the iterative process, making them ineffective under low signal-to-noise ration (SNR) conditions. To address this limitation, a period-refined maximum corrlated kurtosis deconvolution (PRMCKD) method is proposed. This approach refines the iteration period using time synchronous averaging (TSA) for reconolution, enabling accurate extraction of subtle bearing fault features even in strong noise environments. The method operates by first utilizing a filter bank for preliminary localization of the resonance frequency band, thus defining the correct deconvolution direction. With correlated kurtosis as the objective function, and leveraging the period information refined by TSA technology, the optimal filter coefficients are iteratively solved. Rolling bearing fault localization is achieved through the fault features present in the filtered signal. Simulation and experimental analysis results demonstrate that the proposed PRMCKD method offers significant advantages over traditional deconvolution methods for extracting weak fault features in rolling bearings.

The optimized layout of sensor measuring points is of significant importance for the condition monitoring, fault diagnosis and health management of mechanical equipment. Focusing on gear transmission systems, this paper investigates a method for optimizing vibration sensor placement based on the effective independent analysis of gearbox modes and frequency response functions. This method uses the fused signals from the optimized measuring points to monitor the vibration of the gear transmission system. A dynamic model of the gearbox is established, and modal analysis is performed to obtain the mode shapes of the gearbox. These mode shapes are utilized for effective independent analysis to determine an initial sensor layout. Harmonic response analysis is conducted to obtain the frequency response functions of these initial measuring points relative to the main bearing seats. The measuring points are further optimized using principal component analysis and effective independent analysis. The optimized measuring points most sensitive to gear fault excitation are selected. Spectral weighted fusion is performed based on the importance weights of the optimized measuring points. The fused spectrum then enables vibration monitoring of the gear transmission. Analysis of measured data from a bevel gear transmission test rig demonstrates that, compared to the fused spectra from arbitrarily selected measuring point groups, the fused spectrum from the optimized measuring point group exhibits a larger overall response amplitude. This indicates a superior monitoring effect for the oprational status of gear transmission.

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.

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.

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.

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.

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.