To address the limitations of deep neural networks in terms of interpretability and the inability of current interpretable networks to perform cross-domain diagnosis tasks, this paper proposes an interpretable triple feature extractor transfer network(ITFETN). For the interpretability challenge, a multi-layer sparse coding model is established, and its iterative solving algorithm is derived. By unrolling the fast iterative soft thresholding algorithm, an equivalent network form of the sparse coding model soving algorithm is obtained. This equivalent network then serves as a feature extractor, forming an interpretable algorithm-structure-equivalent network. To tackle the problem of cross-domain tranfer diagnosis, a triple feature extractor strategy is constructed. This strategy is designed to extract the shared features from the source and target domains, as well as their respective private features. Based on the concept of feature adversarial learning, a loss function for the transfer diagnosis task is designed for the effective training of ITFETN. This effectively extracts shared features with minimized distance between the source and target domains for cross-domain diagnosis, thereby achieving interpretable transfer diagnosis tasks. Experimental results demonstrate that ITFETN exhibits improved average accuracy and robustness in two case studies compared to benchmark methods. This confirms its effectiveness in achieving interpretable cross-domain diagnosis.

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.

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.

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.

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.

The Hilbert resonance demodulation (HRD) technique is widely applied in bearing fault diagnosis. Current studies typically evaluate noise reduction methods based on the frequency components of envelope signals. However, there is a lack of quantitative analysis regarding the generation process of these frequency components. This might lead to misattributing HRD-induced effects to noise reduction methods during evaluation. In engineering, the empirical selection of test conditions and analysis parameters often results in missed diagnoses due to improper choices. To address these issues, this study constructs a fault signal model for vibration responses of rolling bearing outer race local faults. Through quantitative analysis of the Hilbert resonance demodulation process, the mapping relationship between signal parameters and envelope amplitude spectra is revealed. By investigating the influence of system physical parameters—such as natural frequency, damping ratio, and rotational speed—on signal characteristics, the correspondence between these parameters and the envelope amplitude spectrum distribution is established. This provides a clearer theoretical foundation for HRD applications. Simulated vibration signals processed by HRD demonstrate consistency with theoretical derivations, and experimental validation confirms the analytical conclusions based on the HRD demodulation mechanism.

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.

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.

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.