Search Results (199)

The inter-shaft bearing is an important component of aero-engine rotor systems. It works between a high-pressure rotor and a low-pressure rotor. Effective fault diagnosis of it is significant for an aero-engine. The casing vibration signals can promptly and intuitively reflect changes in the operational health status of an aero-engine’s support system. However, affected by a complex vibration transmission path and vibration of the dual-rotor, the intrinsic vibration information of the inter-shaft bearing is faced with strong noise and a dual-frequency excitation problem. This excitation is caused by the wide span of vibration source frequency distribution that results from the quite different rotational speeds of the high-pressure rotor and low-pressure rotor. Consequently, most existing fault diagnosis methods cannot effectively extract inter-shaft bearing characteristic frequency information from the casing signal. To solve this problem, this paper proposed the denoised improved envelope spectrum (DIES) method. First, an improved envelope spectrum generated by a spectrum subtraction method is proposed. This method is applied to solve the multi-source interference with wide-band distribution problem under dual-frequency excitation. Then, an improved adaptive-thresholding approach is subsequently applied to the resultant subtracted spectrum, so as to eliminate the influence of random noise in the spectrum. An experiment on a public run-to-failure bearing dataset validates that the proposed method can effectively extract an incipient bearing fault characteristic frequency (FCF) from strong background noise. Furthermore, the experiment on the inter-shaft bearing of an aero-engine test platform validates the effectiveness and superiority of the proposed DIES method. The experimental results demonstrate that this proposed method can clearly extract fault-related information from dual-frequency excitation interference. Even amid strong background noise, it precisely reveals the inter-shaft bearing’s fault-related spectral components. Full article

In this study, the combined influence of vibration direction, feature selection strategy, and the support vector machine (SVM) kernel on the classification accuracy of unbalance faults was investigated. Experiments were carried out on a Jeffcott rotor test rig at a constant speed and under three operating conditions. The overlapping sliding window method was used for raw sample expansion. Features extracted from time domain signals and from the order and power spectra obtained in the frequency domain were ranked using the Kruskal–Wallis algorithm. Based on the feature-ranking results, the three most discriminative features for each domain–axis combination, as well as all nine most discriminative features for each axis in a hybrid manner, were fed into SVM classifiers with different kernels, and their performance was evaluated using ten-fold cross-validation. Classification using vibration signals in the vertical direction had higher accuracy rates than those using signals in the horizontal direction for the feature sets obtained in the same domains. According to the statistical results, feature set selection had a much greater impact on classification accuracy than SVM kernel choice. Power spectrum-based features allowed higher classification accuracies in all SVM algorithms compared to both the time domain features and the order spectrum-based features for detecting unbalance faults. Increasing the number of features or employing hybrid feature selection did not result in a consistent or significant enhancement in overall classification performance. Selecting the right SVM kernel shapes both the model’s flexibility and its fit to the chosen feature space; when this fit is inadequate, classification accuracy may decrease. Consequently, by selecting the appropriate vibration direction, feature set, and SVM kernel, an improvement of up to 67% in unbalance fault classification accuracy was achieved. Full article

Deep learning (DL) and machine learning (ML) have advanced rapidly. This has driven significant progress in intelligent fault diagnosis (IFD) of bearings. However, methods like self-attention have limitations. They only capture features within a single sequence. They fail to effectively extract and fuse time- and frequency-domain characteristics from raw signals. This is a critical bottleneck. To tackle this, a dual-channel cross-attention dynamic fault diagnosis network for time–frequency signals is proposed. This model’s intrinsic correlations between time-domain and frequency-domain features, which overcomes single-sequence limitations. The simulation and experimental data validate the method. It achieves over 95% diagnostic accuracy. It effectively captures complex fault patterns. This work provides a theoretical basis for better fault identification in bearing–rotor systems. Full article

Rotating machinery is essential in various industrial fields, and growing demands for high performance under harsh operating conditions have heightened interest in fault diagnosis and prognostic technologies. However, a major challenge in fault diagnosis research lies in the scarcity of data, primarily due to the inability to deliberately introduce faults into machines during actual operation. In this study, a physical model is proposed to realistically simulate the system behavior of a ship’s turbo-rotating machinery by coupling the torsional and lateral vibrations of the rotor. While previous studies employed simplified single-shaft models, the proposed model adopted gear mesh interactions to reflect the coupling behavior between shafts. Furthermore, the time-domain response of the system is analyzed through state-space transformation. The proposed model was applied to simulate imbalance and gear teeth damage conditions that may occur in marine turbo-rotating systems and the results were compared with those under normal operating conditions. The analysis confirmed that the model effectively reproduces fault-induced dynamic characteristics. By enabling rapid implementation of various fault conditions and efficient data acquisition data, the proposed model is expected to contribute to enhancing the reliability of fault diagnosis and prognostic research. Full article

The construction of pumped storage power stations (PSPSs) is undergoing rapid expansion globally. Detecting operational faults and defects in pumped storage units is critical, as effective diagnostic methods can not only identify fault types quickly and accurately but also significantly reduce maintenance costs. This study leverages the symmetry characteristics in the vibration signals of pumped storage units to enhance fault diagnosis accuracy. To address the challenges of selecting the key parameters (e.g., decomposition level and penalty factor) of the variational mode decomposition (VMD) algorithm during vibration signal analysis, this paper proposes an algorithm for an improved subtraction-average-based optimizer (ISABO). By incorporating piecewise linear mapping, the ISABO enhances parameter initialization and, combined with a balanced pool method, mitigates the algorithm’s tendency to converge to local optima. This improvement enables more effective vibration signal denoising and feature extraction. Furthermore, to optimize hyperparameter selection in the bidirectional long short-term memory (BILSTM) network—such as the number of hidden layer units, maximum training epochs, and learning rate—we introduce an ISABO-BILSTM classification model. This approach ensures robust fault diagnosis by fine-tuning the neural network’s critical parameters. The proposed method is validated using vibration data from an operational PSPS. Experimental results demonstrate that the ISABO-BILSTM model achieves an overall fault recognition accuracy of 97.96%, with the following breakdown: normal operation: 96.29%, thrust block loosening: 98.60%, rotor-stator rubbing: 97.34%, and rotor misalignment: 99.59%. These results confirm that the proposed framework significantly improves fault identification accuracy, offering a novel and reliable approach for PSPS unit diagnostics. Full article

Rotor systems are basic in power generation, mechanical, and many other energy equipment and industrial fields. The smooth operation of equipment is linked to the successful operation of technological processes and the safe operation of working equipment. Working conditions nowadays are characterized by intensive rotation speeds, complex structures, and dynamic loads, contributing to different mechanical faults. Detecting such defects in the preliminary stages is inadequate, which could lead to emergencies, high economic loss, and reduced equipment life. Several modern diagnosis methods are widely utilized to monitor the condition in real-time mode, such as vibration parameter analysis, temperature deviation analysis, acoustic emission analysis, and other operational parameter analyses, to avoid the possibility of rotor failure. Some techniques like the vibration signal analysis method, spectral analysis, thermography, ultrasound diagnosis, and machine learning algorithms for predicting failure are of particular interest among them. These techniques allow the defects to be identified immediately and constitute effective preventive maintenance plans, thus significantly enhancing the reliability and economic efficiency of the rotor system operations. This current work is devoted to studying modern diagnostic methods of rotor systems, considering the areas of their realization that are used. This review discusses the theory of the applied methods, advantages, limitations, and the perspective of their further development in innovation integration. It aims to critically analyze and comprehensively systematize methods for energy-consuming rotor equipment condition monitoring that will enhance the efficiency of managing technical conditions for the main components of modern energy systems. Full article

In this paper, we introduce a health parameter and estimation algorithm to assess the severity of stator turn-to-turn/inter-turn short-circuit (TTSC) faults in wound-rotor synchronous generators (WRSG). Our methodology establishes criteria for evaluating the severity of stator TTSC faults in WRSG and provides a specific solution for estimating both the severity of these faults and the resultant power loss. Our assessment methodology directly reflects the intrinsic impact of stator TTSC faults on the WRSG, offering enhanced efficiency, accuracy, and resilience to interference compared with traditional methods in estimating and gauging the TTSC severity. First, we demonstrate that it is impossible to determine the two fault parameters of the WRSG stator TTSC faults solely based on the voltage and current measurements. Subsequently, we introduce a novel health parameter for the WRSG stator TTSC faults and show that for a given generator and load, the dynamics of voltage and current during these faults as well as the resulting power loss are determined by this health parameter. We then detail the characteristics of the proposed health parameter and criteria for evaluating the severity of the WRSG stator TTSC faults. Furthermore, we present an estimation algorithm that is capable of accurately estimating the health parameter and power loss, demonstrating its minimal estimation error. Finally, we provide a comprehensive set of simulation results, including Monte Carlo results, to validate our proposed methodology and illustrate that our approach offers significant improvements in terms of the efficiency, accuracy, and robustness of the WRSG stator TTSC fault detection and isolation (FDI) over conventional methods. Full article

DFIG rotor windings face high stress and transients from back-to-back converters, causing inter-turn short circuit (ITSC) faults. Rapid rotor-side dynamics, combined with the unique capability of DFIG to operate in multiple modes, make the fault detection in rotor windings more challenging. This paper presents a comprehensive methodology for online ITSC fault diagnosis in DFIG rotor windings based on zero-sequence current (ZSC) component analysis under variable operating conditions. Fault features are identified and defined through the analytical evaluation of the DFIG mathematical model. Further, a simple yet effective algorithm is presented for online implementation of the proposed methodology. Finally, the simulation of the DFIG model is carried out in MATLAB/Simulink under both sub-synchronous and super-synchronous modes, covering a range of variable loads and low-frequency conditions, along with different fault severity levels of ITSC in rotor windings. Simulation results confirm the effectiveness of the proposed methodology for online ITSC fault detection at a low-severity stage and precise location identification of the faulty phase within the DFIG rotor windings under both sub-synchronous and super-synchronous modes. Full article

To address the issue of control inaccuracy caused by the zero-offset fault in current sensors within the multiphase brushless DC motor (BLDCM) drive system, this paper proposes a fault diagnosis and fault-tolerant control method based on current signals. Different from traditional solutions that rely on hardware redundancy or precise modeling, this method constructs a dual-channel fault diagnosis framework by integrating the steady-state amplitude offset of the phase current after the fault and the abnormal characteristics of dynamic sector switching. Firstly, sliding time window monitoring is used to identify steady-state amplitude anomalies and locate faulty sectors. Subsequently, an algorithm for detecting the difference in current changes during sector switching is designed, and a logic interlocking verification mechanism is combined to eliminate false triggering and accurately locate single or multiple fault phases. Furthermore, based on the diagnostic information, a repeated iterative online correction method is adopted to restore the accuracy of the current measurement. This method only relies on phase current signals and rotor position information, does not require additional hardware support or accurate system models, and is not affected by the nonlinear characteristics of the motor. Finally, the experimental verification was carried out on a nine-phase BLDCM drive system. Experimental results indicate that the torque fluctuation of the system can be controlled within 5% through the fault-tolerant control strategy. Full article

Motor Current Signature Analysis (MCSA) is a commonly used non-invasive method for diagnosing faults in electric motors. Although MCSA provides significant advantages—current signals are easy to acquire and inherently robust against noise—this study aims to further enhance its diagnostic capabilities by focusing on symmetrical components. Three-phase stator current signals are converted into zero, positive, and negative sequence components, and their time-domain feature vectors are systematically integrated into a single image representation. A Convolutional Neural Network (CNN) is then employed for fault classification. The proposed method is model-free, requiring no explicit motor model, which offers greater flexibility compared to model-based techniques. Validation experiments were conducted on a rotor kit test bench under seven different conditions (one healthy condition and six mechanical/electrical fault conditions), with fault severities chosen to reflect practical scenarios. The symmetrical components-based image classification method demonstrated superior performance, achieving 99.76% classification accuracy and outperforming a widely used Short-Time Fourier Transform (STFT)-based spectrogram approach. These findings highlight that integrating all symmetrical component information into one image effectively captures each fault’s distinct behavior, enabling reliable diagnostic outcomes. By leveraging the distinct variations in zero, positive, and negative components under fault conditions, the proposed method offers a powerful, accurate, and non-invasive framework for real-time motor fault diagnosis in industrial applications. Full article

In this paper, a fault diagnosis method for permanent magnet synchronous motors is proposed, combining wavelet packet transform (WPT) energy feature extraction and a genetic algorithm (GA)-optimized back propagation (BP) neural network. Firstly, for the common types of motor faults (turn-to-turn short-circuit, phase-to-phase short-circuit, loss of magnetism, inverter open-circuit, rotor eccentricity), a corresponding motor fault model is established. The stator current signals during motor operation are analyzed using wavelet packet transform, and energy features are extracted from them as feature vectors for fault diagnosis. Then, a BP neural network is constructed, and a genetic algorithm is used to optimize its initial weights and thresholds, thereby improving the network’s classification accuracy. The results show that the GA-BP model outperforms the SSA-PNN diagnostic model in terms of fault classification accuracy. In particular, for the diagnosis of normal operation, inverter open-circuit, and demagnetization faults, the accuracy rate reaches 100%. This method demonstrates high diagnostic accuracy and practical application value. Full article

This paper proposes a hybrid adaptive robust observation (HARO)-based sensorless control strategy of a five-phase fault-tolerant permanent-magnet (FPFTPM) motor for electric agricultural equipment applications under various operating conditions, including fault conditions. Regarding fault-tolerant sensorless control, the existing studies usually treat fault-tolerant control and sensorless control as two independent units rather than a unified system, which makes the algorithm complex. In addition, under the traditional fault-tolerant algorithm, the system needs to switch after diagnosis when the fault occurs, which leads to a degraded sensorless control performance. Hence, this paper proposes a fault-tolerant sensorless control strategy that can achieve the whole speed range without fault-tolerant switching. At zero/low speed, a disturbance adaptive controller (DAC) architecture is developed by treating phase faults as system disturbances, where robust controllers and extended state observer (ESO) collaboratively suppress speed and position errors. At medium/high speeds, this paper provides a steady-healthy SMO, which combines the enhanced observer and universal phase-locked loop (PLL) without phase compensation. With above designs, the proposed strategy can significantly improve the estimated accuracy of rotor position under normal conditions and fault circumstances, while simplifying the complexity of the fault-tolerant sensorless algorithm. Furthermore, the proposed strategy is verified based on the experimental platform of the FPFTPM motor drive system. The experimental results show that compared with the traditional method, the torque ripple and position error are reduced by nearly 20% and 60%, respectively, at zero-low speed and medium-high speed, and the torque ripple is reduced by 55% during fault operation, which verifies the robustness and effectiveness of the proposed method. Full article

Since permanent magnets and rotors are core components of electric vehicle drive motors, accurate diagnosis of demagnetization and eccentricity faults is crucial for ensuring the safe operation of electric vehicles. Currently, intelligent diagnostic methods based on three-phase current signals have been widely adopted due to their advantages of easy acquisition, low cost, and non-invasiveness. However, in practical applications, the fault characteristics in current signals are relatively weak, leading to diagnostic performance that falls short of expected standards. To address this issue and improve diagnostic accuracy, this paper proposes a novel diagnostic method. First, branch current is utilized as the data source for diagnosis to enhance the fault characteristics of the diagnostic signal. Next, a dual-modal feature extraction module is constructed, employing Variational Mode Decomposition (VMD) and Fast Fourier Transform (FFT) to concatenate the input branch current along the feature dimension in both the time and frequency domains, achieving nonlinear coupling of time–frequency features. Finally, to further improve diagnostic accuracy, a cascaded convolutional neural network based on dilated convolutional layers and multi-scale convolutional layers is designed as the diagnostic model. Experimental results show that the method proposed in this paper achieves a diagnostic accuracy of 98.6%, with a misjudgment rate of only about 2% and no overlapping feature results. Compared with existing methods, the method proposed in this paper can extract higher-quality fault features, has better diagnostic accuracy, a lower misjudgment rate, and more excellent feature separation ability, demonstrating great potential in intelligent fault diagnosis and maintenance of electric vehicles. Full article

To address the challenge of extracting adaptive fault features for unmanned aerial vehicle (UAV) rotor motor bearings and to meet the high accuracy requirements of bearing fault diagnosis, this paper proposes a neural network-based bearing fault diagnosis method using WPT-CEEMD-CNN-LSTM. Initially, the method applies multiple noise reduction processes to the original vibration signals and enhances their time–frequency resolution through Wavelet Packet Transform (WPT) and Complete Ensemble Empirical Mode Decomposition (CEEMD). This effectively removes noise and generates a high-quality dataset. Subsequently, a Convolutional Neural Network (CNN) is employed to automatically extract deep features, while a Long Short-Term Memory (LSTM) network is used for the time-series modeling, thereby constructing an accurate rotor motor bearing fault diagnosis model. The experimental results demonstrate that the fault diagnosis accuracy of this method reaches 96.67%, which is significantly higher than that of the traditional CNN (85%), LSTM (51.33%), and the CEEMD-CNN-LSTM model with single-signal noise reduction (77.33%). This method also exhibits stronger fault identification and generalization capabilities. This study confirms the effectiveness of combining WPT-CEEMD with CNN-LSTM deep learning techniques for UAV bearing fault diagnosis, providing a high-precision and stable diagnostic solution for UAV health monitoring. Full article

With the increasing complexity of offshore wind turbine structures and the rapid expansion of wind power projects, efficient, reliable, and robust fault diagnosis and condition monitoring methods have become crucial for effective operation and maintenance management. Wind turbine condition monitoring plays a pivotal role in improving operational efficiency. However, most existing fault diagnosis techniques based on vibration signals are designed for rotating mechanical equipment operating at constant speeds. In contrast, offshore wind turbines experience continuously varying speeds, especially during start-up, shutdown, and under fluctuating wind conditions, leading to rotor speed variations that complicate monitoring. This paper presents a comprehensive analysis of the vibration and fault characteristics of key components in the main drivetrain of offshore wind turbines, with a particular focus on monitoring non-stationary (variable speed) operations. Unlike conventional approaches, this work specifically addresses the challenges posed by the dynamic operating conditions of offshore wind turbines, providing insights into multi-component vibration signal feature extraction and fault diagnosis under variable-speed scenarios. The comparative analysis offered in this paper highlights the limitations of current methods and outlines key directions for future research, emphasizing practical solutions for fault diagnosis and condition monitoring in offshore wind turbine operations under variable-speed conditions. This study not only fills a gap in the current literature but also provides valuable guidance for enhancing the reliability and efficiency of offshore wind turbine maintenance. Full article