Liquid rocket engine turbopumps operate under severe non-stationary conditions, making it challenging for traditional vibration signal analysis methods to effectively extract fault features. To address this challenge, the cyclostationary random signal model is extended and a generalized cyclostationary analysis framework is established. This framework preserves the advantages of cyclostationary methods in fault diagnosis while broadening their applicability to non-stationary operating regimes. Focusing on vibration signal modeling, fault feature extraction, and characterization, a comprehensive generalized cyclostationary analysis framework specifically for rocket turbopump fault diagnosis is proposed. The superiority and validity of the established theoreical system are demonstrated through a cryogenic bearing operation experiment on a rocket turbopump and a cavitation fault simulation test on a centrifugal pump. Results indicate that vibration signals from rotating machinery can be regarded as approximately cyclostationary processes subject to time warping, which can be further transformed into modulated cyclostationary signals. In the rocket turbopump cryogenic bearing operation experiment, fault feature signals are extracted using the proposed blind adaptive cyclostationary-nonstationary signal extraction method. Its order-frequency spectral correlation map clearly detects spectral lines corresponding to the fundamental train frequency (0.42 Hz) and the ball pass frequency outer race (5.08 Hz). In the centrifugal pump cavitation fault simulation experiment, the proposed high-precision reassigned spectral correlation estimation technique enhances the localization of the blade-pass frequency (197 Hz) in the spectral correlation map. Furthermore, it reliably identifies fault features even under severe noise induced by increasing cavitation levels.

Planetary gear transmission systems are extensively used in industrial applications. Due to their compact and complex configurations, mechanical components are prone to failure during long-term operation. Compared to fixed-axis gear systems, planetary gear systems exhibit multiple excitation sources and time-varying signal transmission paths stemming from their intricate structural and kinematic characteristics. Consequently, condition monitoring techniques based on fixed vibration measurement points face significant challenges in compound fault diagnosis, especially when a planet gear fault is coupled with a bearing fault. To address these issues, this study proposes a non-contact torsional vibration monitoring and residual vibration analysis method utilizing laser doppler vibrometry (LDV). The laser beam is positioned on the low-speed shaft surface to directly acquire torsional vibration information from the gear system. To mitigate the impact of measurement noise on fault feature extraction, a hybrid denoising strategy combining cepstrum-based soft-threshold editing and median filtering is developed to suppress pseudo-vibration artifacts and random impulse noise, respectively. For different types of compound faults, a progressive residual vibration decomposition framework is established. This framework systematically peels off residual broadband response and residual meshing sideband components from the torsional vibration signal. Specifically, optimized filtering is applied to the broadband response obtained via cepstrum short-pass to extract the second-order cyclostationary features of bearing faults. Concurrently, a phase self-demodulation-based order domain resampling method is proposed to highlight gear fault features by reconstructing meshing sideband residual signals of different orders. Experiments involving tooth spalling on planetary gears and raceway spalling on the sun gear bearing demonstrate that the proposed method can effectively achieve non-contact compound fault diagnosis for planetary gear systems. Compared to conventional flexible synchronous averaging and accelerometer-based methods, the proposed approach exhibits superior performance in early-stage planet gear fault detection under varying speeds.

Bearings are critical bogie components, making their early fault detection particularly important. This paper proposes an early fault detection method for bearings based on a relation network (RN). A health status detection relation network model is designed to effectively extract bearing condition features and measure the nonlinear distance between these features. In the offline modeling phase, normal samples from the bearing are collected for training, allowing the model to learn the nonlinear distances among the healthy state sample features. During the online monitoring phase, samples from the current operating state are acquired, and a relation score is obtained as a health indicator for the bearing condition. The 3σ criterion is then applied to determine the health indicator threshold for detecting the bearing health status and identifying faults promptly. Experiments were conducted on the XJTU-SY rolling bearing full-lifecycle dataset. Results show that, compared to methods like root mean square, kurtosis, and stacked autoencoders, the health indicator of the proposed method is more sensitive to early faults and exhibits better monotonicity and trend. Furthermore, in comparison with methods such as Isolation Forest, Support Vector Machine, and stacked autoencoders, the proposed method detects the first fault occurrence earlier, demonstrating considerable practical value.

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.

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.

The increasing complexity of intelligent equipment and evolving operation and maintenance demands within Industry 4.0 highlight the inadequate adaptability of traditional maintenance decision-making methods in dynamic environments. Reinforcement learning (RL)-based maintence decision-making technology offers a paradigm for intelligent equipment maintenance by enabling autonomous strategy optimization through environmental interaction. This paper systematically explores the integration of RL theory and maintenance decision-making, focuses on 76 peer-reviewed articles published between 1954 and 2024. Core RL algorithms, including SARSA, Q-Learning, and Actor-Critic, are thoroughly examined and analyzed. The current state of intelligent equipment maintenance decision-making technology is also analyzed in depth. Typical application scenarios for RL in equipment maintenance decision-making are comprehensively dissected across four key areas: industrial manufacturing, energy, aerospace, and transportation. The study also identifies and discusses the core challenges facing current technology, such as algorithm convergence speed, computational efficiency, model interpretability, and issues related to data acquisition and privacy. This research provides a theoretical reference for algorithm innovation and engineering implementation in the field of intelligent operation and maintenance, fostering the deeper application of RL in maintenance decision-making.

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.

Current research on fault diagnosis for aircraft complex motion mechanisms primarily focuses on system functional failure analysis, neglecting a comprehensive understanding of the correlation between motion characteristics and actual faults. This study investigates fault diagnosis methods for complex motion mechanisms and proposes a three-tiered framework encompassing data generation, feature processing and data analysis to address this limitation. The framework utilizes dynamic modeling and a fault parameter system to generate a dataset of time-series signals representing typical fault conditions. One-dimensional time-series data are mapped using two-dimensional image conversion methods, constructing multidimensional tensors through feature-level fusion based on sensor types and feature extraction methods of the complex motion mechanisms. A deep learning-based fault diagnosis model is employed for precise fault identification of complex motion mechanisms. This framework further incorporates collaborative feature transformations using Gramian angular fields and Markov transition fields, as well as residual network models with channel and spatial attention mechanisms. Experimental validation using a landing gear lower strut lock mechanism demonstrates high accuracy, exceeding 0.9566 at a 95% confidence level, thus validating the feasibility of this approach for fault diagnosis in aircraft complex motion mechanisms. Ablation experiments confirm the effectiveness of each component, highlighting the overall superiority of the proposed framework.

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.

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%.