Component-stacked rotor systems are coupled through shared load paths and vibration transmission. Degradation in any component can change system-level structural parameters and introduce uncertainty into their evolution, thereby reshaping the measured features and apparent fault patterns. Fault identification should therefore account for degradation effects. In this study, we refer to this objective as degradation-induced fault identification. A key challenge is that failure histories capturing progressive degradation are often scarce. Purely data-driven models trained on such samples may learn feature distributions that do not adequately characterize degradation evolution and its associated fault modes. To address this issue, we develop a mechanism-informed generative modeling framework. In the physical model, structural parameters are modeled as stochastic variables following specified probability distributions, enabling the augmented data to better cover the underlying distribution of degradation-induced fault states. Additionally, we develop an uncertainty-guided attention mechanism that concentrates on long-term dependencies in high-uncertainty feature regions. It quantifies the uncertainty propagation from structural parameters to the learned feature space, and provides interpretable insights into degradation-induced fault manifestations. By integrating distributional parameter modeling with physical knowledge, the framework characterizes degradation variability and evolution within a unified model, demonstrating promising applicability in multi-component systems.

This paper proposes a fault-tolerant direct torque control strategy based on an improved sparrow search algorithm (ISSA) and super-twisting sliding mode control to address torque ripple and speed response issues in direct torque control (DTC) of five-phase permanent magnet synchronous motor (PMSM). By minimising torque ripple and optimising speed response under load conditions, the traditional dual-hysteresis DTC method is improved, reducing steady-state torque ripple and overshoot in transient speed response. To handle complete rotor position sensor failures, a fault-tolerant control scheme is proposed. A super-twisting sliding mode controller is designed for the flux and torque loops of the DTC, and the improved sparrow search algorithm optimises three controller parameters for the inner and outer loops. The impact of controller complexity on the computational burden is discussed. This ensures that both speed and torque can quickly recover to normal operation after significant disturbances, effectively eliminating chattering. Simulation results demonstrate that the proposed method significantly reduces steady-state torque ripple, achieves rapid speed response, and provides fault tolerance in the event of rotor position sensor failure.

Rotor skidding behavior can exacerbate bearing wear and failure, leading to equipment instability, making research on root-cause tracing and regulation of rotor skidding essential. However, rotor skidding behavior is coupling influenced by multiple factors, posing significant challenges in precisely identifying the root causes and formulating effective regulation measures. To address these challenges, a rotor skidding root-cause tracing and regulation method based on supervised fine-tuning of large language models (LLMs) is proposed in this paper. Firstly, nearly 200,000 skidding text knowledge is constructed based on the established rotor skidding parameter classification rules and skidding-domain-specific prompt. Subsequently, the supervised fine-tuning of LLM is implemented using the constructed skidding knowledge combined with the QLoRA algorithm. Finally, the fine-tuned LLM is employed to achieve root-cause tracing and regulation under the coupling effects of multiple factors for rotor skidding. Specifically, the effectiveness of the proposed supervised fine-tuning strategy is further validated using the designed semantic knowledge similarity metrics.

Bearing skidding is a frequent phenomenon in rotating machinery, which causes equipment motion instability and bearing wear failure. Due to the difficulties in the manufacture of high-speed flexible rotor test rig, measurement of bearing motion parameters and complex structure of angular contact ball bearing (ACBB), which makes the research on double-piece inner ring ACBB skidding challenging. This paper studies the ACBB skidding mechanism from the aspect of rotor characteristics, and first finds the resonance skidding phenomenon. Firstly, a high-speed flexible rotor test rig is designed and built to study the ACBB skidding mechanism, and the influence of foreign matter on the bearing cage slip rate is systematically studied. Then, the bearing cage speed considering the influence of lubricating oil pollution is accurately measured based on the adaptive fractional short-time Fourier transform. Finally, bearing skidding diagnosis under variable working conditions is realized based on the deep meta-transfer learning with feature enhanced generative adversarial network and average deflection power threshold. These proposed strategies systematically solve some problems in the research field of ACBB skidding, which has high practical significance and theoretical guidance value.

The multifactor coupling influence on the skidding behavior of aeroengine rotors presents significant challenges in locating the skidding causes and developing effective skidding suppression measures. However, ongoing research into fault mechanism and knowledge graph (KG) facilitates the accurate tracing of complex faults. We propose a heterogeneous KG inference-assisted skidding tracing and regulation strategy for aeroengine rotor. First, a skidding heterogeneous KG is constructed based on the text and data knowledge, in which the skidding level classification rules are determined for the first time. Second, we design an adaptive distributed metalearning algorithm to extract data features by combining the structural characteristics of the skidding KG. Third, few-shot knowledge inference is performed using the relation-metalearning graph convolutional network. Finally, we develop skidding suppression measures by tracing the input knowledge under unknown working states, enabling effective regulation of skidding behavior.

Bearing skidding is the primary factor restricting the development of aeroengines toward ultrahigh speed, low friction, and lightweight. Compared to typical bearing faults, analysis of bearing skidding presents greater challenges due to the weak signal properties, significant time-varying characteristics and coupling influence of multiple factors. It is crucial to fully utilize multisource signals to enhance skidding features and capture time-varying characteristics. This article proposes a prior knowledge-embedded dual feedback spatial-temporal graph convolutional network (DFSTGCN) for skidding assessment. Unlike existing adjacency matrix construction strategies, the correlation between multisource signals is described based on multiple prior knowledge, which includes dynamic model, structural dynamics, and expert experience. Furthermore, a DFSTGCN is designed to simultaneously focus on the spatial and temporal dependencies of time-varying skidding data. Specifically, a dual feedback mechanism that includes prediction error ratio and uncertainty loss function is employed to improve the generalization performance of skidding prediction model. The effectiveness of the proposed strategy is validated under different working conditions.

The demand for bearing skidding diagnosis is widely present in aeroengines operating at high-speed and light-load conditions. However, the weak and time-varying characteristics of skidding signal raise challenges for accurate diagnosis. To address these issues, we propose a synergistic TransGCN strategy to extract rich feature information from time-varying weak bearing skidding signals. Unlike existing methods, the prior knowledge obtained from bearing skidding analysis and the alternate integration and synergistic optimization of various advantages are used to enhance algorithm performance. First, an adaptive chirplet transform is designed to measure the time-varying cage slip rate. Second, the skidding sensitive characteristics are determined, and the variation ranges of slip rate sensitivity are employed as prior knowledge to calculate the fusion weights of multisource information. Then, an unsupervised deep feature representation network is constructed to analyze the complex correlation of bearing skidding signals. Finally, a synergistic TransGCN is developed by alternately integrating and synergistic optimizing Bayesformer and graph convolutional network. The superiority of the proposed strategy has been verified.

This paper proposes an original intelligent robust control method for a kind of aviation electric fuel pump (AEFP) systems, in order to realize on-demand fuel supply for aircraft engines, accurately and robustly. By considering the feature of AEFP, the intelligent robust control method is in a cascaded structure, which combines a novel second order integral sliding mode control (SOISMC) approach with uncertainty and disturbance estimator (UDE) strategy, together with a new multi-objective variable speed grey wolf optimization algorithm (MOVSGWO). To let the current loop of AEFP possess both rapidity and robustness, the SOISMC is presented based on an innovative second order integral sliding mode manifold. To realize that AEFP has strong robustness to both the changing of speed command and the pulsation in fuel pump, UDE is utilized for the speed loop of AEFP. The primary parameters in the intelligent robust controller, namely, UDE-SOISMC controller, are optimized by MOVSGWO. The MOVSGWO is a modified grey wolf optimization algorithm with variable speed by particle swarm optimization. Mismatched uncertainties and disturbances in AEFP can be handled by the proposed robust method. The calculation of instantaneous flow and dynamic torque in AEFP is illustrated. Simulation and experimental results verify the effectiveness of the UDE-SOISMC method.

The coupling analysis of model performance degradation and fault diagnosis under variable working states, known as dynamic fault diagnosis, is widely recognized as urgent need. The inadequate exploration of missing labeled samples and the intricate connection between degradation signals and fault signals has resulted in limited research on these issues. Therefore, it is necessary to design an effective method that can be used for degradation state identification and cross-domain dynamic fault diagnosis. This article proposes a novel strategy based on the optimized linear parameter-varying model and knowledge-enhanced graph convolutional adversarial network (KEGCAN). First, a degradation simulation model of the rotor system is established by analyzing the changes in contact stiffness and support stiffness caused by bolt loosening and bearing wear, respectively. Then, the proposed KEGCAN is developed based on prior knowledge from the time-frequency features of simulation data, taking advantage of the powerful capabilities of simulation model and graph convolutional network. Finally, high-precision cross-domain online dynamic fault diagnosis is achieved using both experimental data collected from the rotor system test rig and simulation data collected from the degradation simulation model. The results indicate that the proposed strategy has better diagnosis accuracy than the state-of-the-art algorithms.

Aiming at the skidding phenomenon of cylindrical roller bearing under high speed and light load, the explicit dynamic analysis software LS-DYNA which is suitable for nonlinear dynamic impact problem is used to carry out simulation and multi-factor analysis. According to the analysis results, the measures to reduce the slip rate of cylindrical roller bearings are given. In this paper, the influence of various factors on the slip phenomenon is simulated and analyzed. The results show that the reduction of friction coefficient, roller number, pocket clearance, acceleration and other parameters can reduce the slip rate of the cage, while increasing radial load can reduce the slip rate of cage. And excessive friction coefficient, roller number, radial clearance and acceleration will increase the fluctuation of the slip rate of the model. In a certain range, as the speed increases, the slip rate and the speed fluctuation decreases. After the speed increases to a certain extent, the slip rate and the speed fluctuation will also suddenly increase. Appropriate pocket clearance and cage with outer ring guide way are conducive to the stable rotation of the bearing. The increase of temperature will have different effects on the bearing. It is necessary to determine an optimal temperature to reduce the slip rate.

Fault diagnosis of bearing under variable working conditions is widely required in practice, and the combination of working conditions and fault fluctuations increases the complexity of addressing its related problems. By developing a virtual simulation model, a digital twin (DT) can obtain the same or even more information than the physical object at a lower cost. Furthermore, it has great potential in the application of bearing fault diagnosis. In this paper, the DT model of the bearing test rig is robustly established, and the fault diagnosis bearing between the simulation and physical object is realized using the proposed enhanced meta-transfer learning (EMTL). First, the DT model is established through parameter identification and modal testing, and the modeling accuracy of DT model reaching 95.685%. The bearing simulation and experimental data are then collected under the same conditions using the DT model and bearing test rig, and the simulation data with little deviation from the experimental data is obtained. Finally, an attention mechanism and domain adaptation are introduced into the EMTL, with the average accuracy of fault diagnosis of bearing reaching 95.18% with few-label target domain data. The proposed strategy is both theoretically significant and practically useful. The experiment results demonstrate that our method outperforms a series of state-of-the-art methods on the bearing fault diagnosis across various limited data conditions. The proposed strategy effectively solves the few-shot problem, which is both theoretically significant and practically useful.

In order to solve the problem of motor fault caused by extreme low temperature and vibration in the high altitude of aviation propulsion motor, using the intelligent sliding mode fault-tolerant control, a speed controller based on variable exponential power reaching law and an extended sliding mode disturbance observer is designed; furthermore, the parameters are optimized by the adaptive chaotic gray wolf optimization algorithm. In addition, a second-order nonsingular terminal sliding mode observer and a frequency adaptive complex coefficient filter are designed. First, variable exponential power reaching law is helpful to eliminate the speed tracking error and buffeting for the speed controller, the external disturbance can be estimated and compensated using the extended sliding mode disturbance observer, and adaptive chaotic gray wolf optimization algorithm can enhance the accuracy of parameters. Second, for the sake of making the aviation propulsion motor run smoothly in the case of speed sensor fault, second-order nonsingular terminal sliding mode observer is designed to estimate the speed and position of aviation propulsion motor, and adaptive complex coefficient filter is designed to improve the estimation accuracy. Finally, based on MATLAB/Simulink simulation and STM32 motor series experiments, the effectiveness of the proposed observer and controller algorithm is verified.

In order to make full use of fault historical text data, knowledge graph is used to assist fault diagnosis. Text classification, a fundamental method for constructing knowledge graph, is prone to the overfitting issue when dealing with sparse and small sample data such as fault domain data. Therefore, an integrated neural network model based on CNN-BiLSTM (convolutional neural networks and bidirectional long and short-term memory networks) is proposed in this paper. In this model, CNN layers extract local semantic features while BiLSTM layers integrate contextual semantic information. Then, adversarial training and attention mechanism are introduced to enhance model performance and prevent overfitting. The proposed method is verified on two datasets, general text data set THUCNews and bearing fault dataset. Comparative experiments reveal that the model can push text classification accuracy in the bearing fault domain to 90%, thereby contributing to greater efficiency and lower labor costs for fault knowledge graph construction.

The transfer learning method performs better than conventional deep learning when dealing with the few-shot diagnosis situation where obtaining the true bearing defect signal is challenging. In order to leverage transfer learning to overcome the few-shot challenge of variable-condition bearing failure diagnosis, we propose the few-shot fault diagnosis approach based on enhanced meta-transfer learning. First, the network parameters are optimized based on a meta-learner. Second, a meta-learning-based transfer network model is constructed, combined with domain-adaptive methods to obtain a meta-learner with strong generalization ability. Meanwhile, the channel attention module is applied to the feature layer to strengthen the model’s feature expression ability. The proposed method Take advantage of the limited fault feature on small-sample data, while avoiding overfitting and improving the generalization ability. The performance of the proposed approach is verified on the fault data from the low-speed dynamic balance test bench. The consequences indicate that the diagnosis approach based on meta-transfer learning can accurately classify the bearing failures under variable conditions. Contrasted to other approaches, the proposed approaches possess better accuracy and generalization capability.

针对风力机变桨距执行机构突变故障,提出了基于风速估计的自适应状态反馈滑模容错控制策略.首先,设计了基于自适应状态反馈滑模理论的鲁棒主动容错控制器,并结合全阶补偿器对控制律进行设计;然后,利用基于变速灰狼优化算法的组合径向基函数神经网络实现风速估计,可以改善风速测量精度并提高控制系统可靠性;最后,根据线性矩阵不等式和Lyapunov理论对控制器稳定性进行讨论,并与现有控制策略进行比较.仿真结果表明,在健康/故障的变桨距执行机构条件下,所提容错控制方法均能获得较好的控制效果.

In this paper, an intelligent fractional-order integral sliding mode control (FOISMC) strategy based on an improved cascade observer is proposed. First, an FOISMC strategy is designed to control a permanent magnet synchronous motor. It has good tracking performance, is strongly robust, and can effectively reduce chattering. The proposed FOISMC strategy associates strong points of the integral action (which can eliminate steady-state tracking errors) and the fractional calculus (which is flexible). Second, an improved cascade observer is proposed to detect the rotor information with a smaller observation error. The proposed observer combines an adaptive sliding mode observer and an extended high-gain observer. In addition, an improved variable-speed grey wolf optimization algorithm is designed to enhance controller parameters. The effectiveness of the strategy is tested using simulations and an experiment involving model uncertainty and external disturbance.

为有效降低风力机在高风速运行时的不平衡载荷,提出一种基于自适应非奇异智能终端滑模观测器的载荷增广预测控制策略.首先,针对模型不匹配导致的模型预测控制性能下降的问题,将指令跟踪误差与系统状态的变化量增广为状态向量,设计增广预测模型以消除稳态跟踪误差;其次,设计自适应非奇异终端滑模观测器对系统状态进行估计,以提高控制系统的可靠性;然后,设计多目标变速灰狼优化算法同时对控制器和观测器参数寻优;最后,基于Simulink仿真平台验证了所提控制策略的有效性.结果表明,所提控制策略可有效消除稳态误差,缩短调节时间并提高控制性能.

During the operation of an aero-engine, bearing skidding often occurs, causing varying degrees of damage to the rolling elements and raceways, affecting the life of the bearing, and severely restricting the reliability of the engine. In the actual engine operation process, due to the complexity of the system, it is difficult to directly install various sensors on the bearing to measure the speed, vibrating and other signals of the main shaft bearings. At the same time, there are situations where signals are mixed. For this reason, the BSS theory is used to separate the bearing vibration signal, and the cage slipping rate is obtained based on the relationship between the cage slipping and the fault characteristic frequency.

Aiming at the maximum power point tracking for wind turbine, a sensorless intelligent second-order integral sliding mode control based on wind speed estimation is proposed in this article. The maximum wind energy capture is realized by controlling permanent magnet synchronous motor to adjust the speed of wind turbine. First, an intelligent second-order integral sliding mode control is designed for the speed loop and current loop control, which has fast convergence speed, strong robustness and can effectively reduce chattering. Second, a novel cascade observer based on direct sliding mode observer and extended high-gain observer is used to estimate the rotor speed and position. Besides, combined radial basis function neural network is used to estimate the valid value of wind speed. Both simulation and experiment are implemented, which verify the effectiveness of the proposed strategy under the condition of considering both model uncertainty and external disturbance.

A robust control strategy using the second-order integral sliding mode control(SOISMC)based on the variable speed grey wolf optimization(VGWO)is proposed. The aim is to maximize the wind power extraction of wind turbine. Firstly,according to the uncertainty model of wind turbine,a SOISMC torque controller with fast convergence speed,strong robustness and effective chattering reduction is designed,which ensures that the torque controller can effectively track the reference speed. Secondly,given the strong local search ability of the grey wolf optimization (GWO) and the fast convergence speed and strong global search ability of the particle swarm optimization(PSO),the speed component of PSO is introduced into GWO,and VGWO with fast convergence speed,high solution accuracy and strong global search ability is used to optimize the parameters of wind turbine torque controller. Finally,the simulation is implemented based on Simulink/SimPowerSystem. The results demonstrate the effectiveness of the proposed strategy under both external disturbance and model uncertainty.