Search Results (462)

This article discusses the development of an enhanced monitoring and control system for helicopter turboshaft engines during flight operations, leveraging advanced neural network techniques. The research involves a comprehensive mathematical model that effectively simulates various failure scenarios, including single and cascading failure, such as disconnections of gas-generator rotor sensors. The model employs differential equations to incorporate time-varying coefficients and account for external disturbances, ensuring accurate representation of engine behavior under different operational conditions. This study validates the NARX neural network architecture with a backpropagation training algorithm, achieving 99.3% accuracy in fault detection. A comparative analysis of the genetic algorithms indicates that the proposed algorithm outperforms others by 4.19% in accuracy and exhibits superior performance metrics, including a lower loss. Hardware-in-the-loop simulations in Matlab Simulink confirm the effectiveness of the model, showing average errors of 1.04% and 2.58% at 15 °C and 24 °C, respectively, with high precision (0.987), recall (1.0), F1-score (0.993), and an AUC of 0.874. However, the model’s accuracy is sensitive to environmental conditions, and further optimization is needed to improve computational efficiency and generalizability. Future research should focus on enhancing model adaptability and validating performance in real-world scenarios. Full article

Rotating systems are essential components and play a critical role in many industrial sectors. Unbalance is a very common and serious fault that can cause machine downtime, unplanned maintenance, and potential damage to vital rotating machines. Accurately estimating unbalance in rotor–bearing systems is crucial for ensuring the reliable and efficient operation of machinery. This research paper presents a novel approach utilizing artificial neural networks (ANNs) to estimate the unbalance masses in a multidisk system based on simulation data from a nonlinear rotor–bearing system. Additionally, this study explores the effect of various operating parameters on oil film stability and vibration response through a combination of bifurcation diagrams, spectrum cascades, Poincare maps, and orbit and FFT plots. This study demonstrates the effectiveness of ANNs for unbalance estimation in a fast and accurate way and discusses the potential of ANNs in smart online condition monitoring systems. Full article

Fault ride-through is a prerequisite for ensuring continuous operation of a variable-speed pumped storage unit with a full-size converter (FSC-VSPU) and providing support for the renewable energy and power grid. This paper proposes low-voltage ride through (LVRT) and high-voltage ride through (HVRT) strategies for FSC-VSPU to address this issue. Firstly, the structure of FSC-VSPU and its control strategy under power generation and pumping conditions are described. Subsequently, the fault characteristics of the FSC-VSPU under different operating conditions are analyzed. More stringent fault ride-through technical requirements than those for wind turbines are proposed. On this basis, the fault ride-through strategies of combining fast power drop, rotor energy storage control, power anti-regulation control, dynamic reactive current control, low power protection, and DC crowbar circuit are proposed. Simulation case studies conducted in PSCAD/EMTDC verify the correctness of the theoretical analysis and the effectiveness of the LVRT and HVRT strategies in this paper. Full article

In order to ensure the vibration safety of rotor systems in the next generation of aero-engines and reduce the impact of misalignment faults, the effect of support misalignment on the vibration characteristics of rotor systems under ultra-high operating speeds is investigated in this paper. Firstly, an analytical excitation model of the rotor systems under ultra-high operating speeds is established, considering the impact of the support misalignment. Then, based on the model of the misaligned combined support system, the dynamic model of the flexible discontinuous rotor support system with the support misalignment is presented. Subsequently, based on the established model, the effects of support parameters and support misalignment amounts on the vibration characteristics of the rotor support system are analyzed. Finally, experimental validation of the research findings is conducted. The research result shows that the support misalignment increases the vibration response of the rotor, reduces the vibration reduction efficiency of the combined support system, and consequently decreases the vibration safety of the rotor support system. Full article

Rotor bar breakage in induction motors is often detected by analysing the signatures in the stator current. However, due to the alteration of the current spectrum, traditional methods may fail when inverter-fed motors operate with closed-loop control using a cascade structure to regulate the speed. In this paper, the potential of zero-sequence voltage analysis to detect this fault is investigated, and a new index to quantify the severity of the fault based on this signal is proposed. Signals from motors operating under different control strategies and signals from motors powered from the mains are considered to verify the robustness of the proposed fault severity index. As a result, in all the analysed conditions the value of the proposed index for the healthy motor is found to be approximately 0.010, while for the faulty machine it is between 0.110 and 0.252. Full article

This research focuses on the development of a machine learning-based approach for the early diagnosis of blade rubbing in rotary machinery. In this paper, machine learning-based diagnostic methods are used for blade rubbing early diagnosis, and the faults are simulated using experimental models. The experimental conditions were simulated as follows: Excessive rotor vibration is generated by an unbalance mass, and blade rubbing occurs through excessive rotor vibration. Additionally, the severity of blade rubbing was also simulated while increasing the unbalance mass. And then, machine learning-based diagnostic methods were applied and the trends according to the severity of blade rubbing were compared. This paper provides a signal processing method through feature analysis to diagnose blade rubbing conditions in machine learning. It was confirmed that the results of the unbalance and blade rubbing represent different trends, and it is possible to distinguish unbalance from blade rubbing before blade rubbing occurs. The diagnosis using machine learning methods will be applicable to rotating machinery faults like blade rubbing; furthermore, the early diagnosis of blade rubbing will be possible. Full article

The article presents the topic of propeller damage detection on unmanned multirotor drones. Propeller damage is dangerous as it can negatively affect the flight of a drone or lead to hazardous situations. The article proposes a non-invasive method for detecting damage within the drone’s hardware, which utilizes existing sensors in the Internal Measuring Unit (IMU) to classify propeller damage. The classification is performed by using the Bayesian Gaussian Mixture Model (BGMM). In the field of drone propeller damage detection, there is a significant issue of data scarcity due to traditional methods often involving invasive and destructive testing, which can lead to the loss of valuable equipment and high costs. Bayesian methods, such as BGMM, are particularly well-suited to address this issue by effectively handling limited data through incorporating prior knowledge and probabilistic reasoning. Moreover, using the IMU for damage detection is highly advantageous as it eliminates the need for additional sensors, reducing overall costs and preventing added weight that could compromise the drone’s performance. IMUs do not require specific environmental conditions to function properly, making them more versatile and practical for real-world applications. Full article

There are numerous methods for detecting interturn faults (ITFs) in the field winding of synchronous machines (SMs). One effective approach is based on comparing theoretical and measured excitation currents. This method is unaffected by rotor temperature in static excitation SMs. However, this paper investigates the influence of rotor temperature in brushless synchronous machines (BSMs), where rotor temperature significantly impacts the exciter excitation current. Extensive experimental tests were conducted on a special BSM with measurable rotor temperature. Given the challenges of measuring rotor temperature in industrial machines, this paper explores the feasibility of using stator temperature in the exciter field current estimation model. The theoretical exciter field current is calculated using a deep neural network (DNN), which incorporates electrical brushless synchronous generator output values and stator temperature, and it is subsequently compared with the measured exciter field current. This method achieves an error rate below 0.5% under healthy conditions, demonstrating its potential for simple implementation in industrial BSMs for ITF detection. Full article

This paper presents a fixed-time fault-tolerant adaptive neural network control scheme for the Twin-Rotor Multi-Input Multi-Output System (TRMS), which is challenging due to its complex, unstable dynamics and helicopter-like behavior with two degrees of freedom (DOFs). The control objective is to stabilize the TRMS in trajectory tracking in the presence of unknown nonlinear dynamics, external disturbances, and sensor faults. The proposed approach employs the backstepping technique combined with adaptive neural network estimators to achieve fixed-time convergence. The unknown nonlinear functions and disturbances of the system are processed via an adaptive radial basis function neural network (RBFNN), while the sensor faults are actively estimated using robust terms. The developed controller is applied to the TRMS using a decentralized structure where each DOF is controlled independently to simplify the control scheme. Moreover, the parameters of the proposed controller are optimized by the gray-wolf optimization algorithm to ensure high flight performance. The system’s stability analysis is proven using a Lyapunov approach, and simulation results demonstrate the effectiveness of the proposed controller. Full article

According to the characteristics of the rotor system in an aero-engine and the vibrational test requirements of the aero-engine ground test, suitable vibration measurement sensors and test positions were selected. The vibration signals at the casings for the compressor and turbine of a twin-spool turbo-jet engine were collected under the states of maximum power and afterburning respectively, and the power spectrum analysis was carried out to determine the positions and causes of vibration. Furthermore, methods and preventive measures for eliminating vibration have been proposed. The results indicated that the main rotor vibration excited by mass imbalance in the twin-spool turbo-jet engine was significant. Rotor spindle misalignment or rotor radial stiffness unevenness also induced the vibration. The aerodynamic pulse vibration formed by the rotor blades of the first stage of the low pressure compressor was large, and rub induced vibration fault may occur at the turbine rotor seals. Based on the power spectrum analysis technology, the rotor system faults information including the type, position, and the degree can be quickly identified, and useful attempts and explorations have been made to reduce the vibration faults of the twin-spool turbo-jet engine. Full article

The doubly-fed induction generator (DFIG) is vulnerable to overcurrent at the stator winding and overvoltage at the DC link due to voltage drop after the grid fault. The large wind farm may have a capacity of several million MWs, whose tripping yields a notable power imbalance and frequency drop in the power systems, which may be avoided by the low-voltage ride-through (LVRT) strategies implemented with the hardware or software. The latter has the merits of low cost and easy to realize, thus studied in this paper. Considering the grid fault uncertainty and DFIG parameters’ correlation, this paper newly introduces the sliding mode structure into the passive control to improve the performance of the inner current control loop of the rotor-side converter (RSC), thus proposing a passive sliding mode control (P-SMC) based RSC control strategy to improve the LVRT capability of the DFIG. The time domain analysis with different fault severities, i.e., voltage drops, at the point of public coupling (PCC) is performed. The simulation results with the P-SMC control or not are obtained and compared to verify the control effect and the robustness of the proposed LVRT strategy. This study is beneficial for maintaining power system security against fast-increasing wind power. Full article

The uninterrupted operation of induction motors is crucial for industries, ensuring reliability and continuous functionality. To achieve this, we propose an innovative approach that utilizes an efficiency model-based digital shadow system for in situ failure detection and diagnosis (FDD) in induction motors (IMs). The shadow model accurately estimates IM losses and efficiency across various operational conditions. Our proposed method utilizes efficiency as the primary indicator for fault detection, while losses serve as condition indicators for fault diagnosis based on real-time motor parameters and loss sources. We introduce a bond graph as a fault diagnosis network, linking loss sources, motor parameters, and faults. This interconnected approach is the key aspect of our proposed diagnostic method and aims to be used in fault diagnosis as a general method. A case study of a broken rotor bar is used to validate the proposed method using a dataset of five motors. Among these, one motor operates without failure, while the remaining four exhibit broken rotor faults categorized as 1, 2, 3, and 4. The proposed method achieves 99.99% precision in identifying one to four defective rotor bars in IMs. Comparative analysis demonstrates good performance compared to vibration-based FDD approaches. Moreover, our methodology is computationally efficient and aligned with Industry 4.0 requirements. Full article

The inter-shaft bearing is the core component of a high-pressure rotor support system of a high-thrust aero engine. One of the most challenging tasks for a PHM is monitoring its working condition. However, considering that in the bearing rotor system of a high-thrust aero engine bearings are prone to wear failure due to unbalanced or misaligned faults of the rotor system, especially in harsh environments, such as those at high operating loads and high rotation speeds, bearing wear can easily evolve into serious faults. Compared with aero engine fault diagnosis and RUL prediction, relatively little research has been conducted on bearing condition monitoring. In addition, considering how to evaluate future performance states with limited time series data is a key problem. At the same time, the current deep neural network model has the technical challenge of poor interpretability. In order to fill the above gaps, we developed a new framework of a residual space–time feature fusion focusing module named RTCA-Net, which focuses on solving the key problem. It is difficult to accurately monitor the wear state of aero engine inter-shaft bearings under special working conditions in practical engineering. Specifically, firstly, a residual space–time structure module was innovatively designed to capture the characteristic information of the metal dust signal effectively. Secondly, a feature-focusing module was designed. By adjusting the change in the weight coefficient during training, the RTCA-Net framework can select the more useful information for monitoring the wear condition of inter-shaft bearings. Finally, the experimental dataset of metal debris was verified and compared with seven other methods, such as the RTC-Net. The results showed that the proposed RTCA-Net framework has good generalization, superiority, and credibility. Full article

The Meijiayan landslide in Pengshui County within Chongqing City is a medium-scale soil landslide triggered by the excavation of roadbeds. To delve into the influencing factors and assess the stability of this landslide, it is crucial to meticulously map the subterranean geological framework of the area. Such an analysis lays the groundwork for evaluating and mitigating the risks of future landslide instabilities. In this context, the semi-airborne transient electromagnetic method (SATEM), which is complemented by a receiving system mounted on an aerial platform, stands out as an innovative geophysical exploration technique. This method is adept at conducting swift measurements across complex terrains, making it particularly valuable for areas prone to such geological events. This paper presents the utilization of a cutting-edge loop source SATEM system, which was operationalized via a rotor-based unmanned aerial vehicle (UAV). The system was employed to conduct shallow geological structure detection experiments on the Meijiayan landslide. The SATEM detection outcomes have unveiled fluctuations in the electrical distribution across the upper strata, which are indicative of the subsurface geological boundaries, faults, and areas potentially rich in water within the landslide region. These discoveries affirm the viability of utilizing loop source SATEM for the identification of shallow geological structures in regions susceptible to landslides. The findings indicate that while the landslide is currently in a stable condition, it poses a significant risk of movement, especially during the rainy season, with the potential for landslides to be exacerbated by extreme or sustained rainfall events. Full article

External-rotor dual-armature flux-switching PM (ER-DA-FSPM) machines have high torque density and decent fault tolerance, making them promising candidates for in-wheel machine applications in electric vehicles. The torque output and optimal design parameters of ER-DA-FSPM machines are affected by the stator/rotor torque ratio, which is the focus of this paper. Firstly, this paper analyzes airgap flux density harmonics of ER-DA-FSPM to provide a clear insight into the torque-generation mechanism. Then, this paper investigates the influence of torque ratio on average torque under the same copper loss. It is found that the average torque decreases with torque ratio increasing due to the reduction of the positive torque component generated by the sixth airgap field harmonics and the rise in the negative torque component from the eighth harmonics. Moreover, this paper also provides the optimal parameter recommendation to guide the machine design. The split ratio should increase, and the arc of PMs should decrease for a larger torque ratio, whilst the other parameters are hardly influenced. Next, this paper makes a comparison among the ER-DA-FSPM machine, external rotor flux-switching PM (ER-FSPM) machine, and surface-mounted PM (ER-SPM) machines. It shows that the ER-DA-FSPM machine, with the torque ratio being 2, can lead to a much larger total torque. In addition, in the event of rotor winding failure, which is more possible due to the existence of slip rings than stator winding failure, the stator can still provide an average torque larger than that of ER-SPM machine and 92.0% that of the ER-FSPM machine, respectively. Finally, the theoretical analysis is verified by the experiments. Full article