Search Results (86)

Ensuring the reliability and robustness of spacecraft systems remains a key challenge, particularly given the limited feasibility of continuous real-time monitoring during on-orbit operations. In the domain of Fault Detection, Isolation, and Recovery (FDIR), no universal strategy has yet emerged. Traditional approaches often rely on precise, model-based methods executed onboard. This study explores data-driven alternatives for self-diagnosis and fault detection using Machine Learning techniques, focusing on spacecraft Guidance, Navigation, and Control (GNC) subsystems. A high-fidelity functional engineering simulator is employed to generate realistic datasets from typical onboard signals, including sensor and actuator outputs. Fault scenarios are defined based on potential failures in these elements, guiding the data-driven feature extraction and labeling process. Supervised learning algorithms, including Support Vector Machines (SVMs) and Artificial Neural Networks (ANNs), are implemented and benchmarked against a simple threshold-based detection method. Comparative analysis across multiple failure conditions highlights the strengths and limitations of the proposed strategies. Results indicate that Machine Learning techniques are best applied not as replacements for classical methods, but as complementary tools that enhance robustness through higher-level self-diagnostic capabilities. This synergy enables more autonomous and reliable fault management in spacecraft systems. Full article

The energy crisis and environmental pollution have driven the rapid development of new energy vehicles (NEVs). As a core technology for integrating electrification and intelligence in NEVs, the brake-by-wire (BBW) system has become a research hotspot due to its excellent braking energy recovery efficiency and precise active safety control performance. This paper provides a comprehensive review of the research progress in BBW technology for NEVs and provides a forward-looking perspective on its future development. First, the types and structures of the BBW system are introduced, and the development history and representative products are systematically reviewed. Next, this paper focuses on key technologies, such as the design and modeling methods of the BBW system, braking force optimization and distribution strategies, precise actuator control, multi-system coordination, driver operation perception, intelligent decision-making, personalized control, and fault diagnosis and fault-tolerant control. Finally, the main challenges faced in the research of BBW technology for NEVs are analyzed, and future development directions are proposed, providing insights for the optimization designs and industrial application of the BBW system in the future. Full article

This paper presents a novel fault diagnosis technique for remote-controlled robotic arm systems, utilizing deep fuzzy echo state networks (DFESNs) and applies the covariance matrix adaptation evolution strategy (CMA-ES) to optimize the hyperparameters of the DFESN model. The developed DFESN model, optimized via CMA-ES, efficiently performs online fault classification through small datasets and training. The method is evaluated through experiments on a leader–follower robotic arm system, demonstrating high accuracy and efficiency. The faults under consideration include leader sensor fault, communication fault, actuator fault, and follower sensor fault. Only follower sensor data are utilized for fault diagnosis. The DFESN model achieves a mean accuracy of 99.5% with the shortest training and online diagnosis times compared to other methods, making it suitable for real-time fault diagnosis applications. Full article

Distributed interconnected systems are complex dynamic systems where every single subsystem has an impact on other subsystems. Actuators are key components in interconnected dynamic systems, which are prone to faults due to age and unexpected conditions. Therefore, there is motivation to develop an effective diagnosis algorithm for distributed interconnected systems, which is a starting point for predictive maintenance. In this study, an actuator fault estimation approach is proposed for a class of nonlinear interconnected systems with direct feedthrough inputs. Specifically, the original interconnected system is transformed into an augmented system by setting an extended state vector composed of an original state vector and actuator fault vector. An additional control term is used to eliminate the impact from unknown disturbances on the estimator error dynamics. Regional pole constraints are considered in the design of the distributed robust observer so that the poles are placed into a desired stable region. The observer gains are obtained by solving simultaneous linear matrix inequalities. Finally, the effectiveness of the proposed method is demonstrated by simulation studies, and a comparison is also provided. Full article

The kinematic complexity and multi-actuator dependence of Delta-type manipulators render them vulnerable to performance degradation from faults. This study presents a novel approach to Active Fault-Tolerant Control (AFTC) for Delta-type parallel robots, integrating an advanced fault diagnosis system with a robust control strategy. In the first stage, a fault diagnosis system is developed, leveraging a hybrid feature extraction algorithm that combines Wavelet Scattering Networks (WSNs), Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Meta-Learning (ML). This system effectively identifies and classifies faults affecting single actuators, sensors, and multiple components under real-time conditions. The proposed AFTC approach employs a hybrid optimization framework that integrates Genetic Algorithms and Gradient Descent to reconfigure a Type-2 fuzzy controller. Results show that the methodology achieves perfect fault diagnosis accuracy across four classifiers and enhances robot performance by reducing critical degradation to moderate levels under multiple faults. These findings validate the robustness and efficiency of the proposed fault-tolerant control strategy, highlighting its potential for enhancing trajectory tracking accuracy in complex robotic systems under adverse conditions. Full article

Fault diagnosis is crucial for ensuring the reliability and safety of wind energy conversion systems (WECSs). However, existing methods are often specific to components or specific types of wind turbines and face challenges, such as difficulty in threshold setting and low accuracy in diagnosing faults at early stages. To address these challenges, this paper proposes a novel fault diagnosis method based on self-organizing neural networks (SONNs) and probability density functions (PDFs). First, an improved set-valued observer (ISVO) is designed to accurately estimate the states of WECSs, considering the time delay and unknown nonlinearity of overall model. Then, the PDF is derived by fitting the estimation error data to characterize three common multiplicative faults of the pitch system actuators. Two types of SONNs are developed to cluster the parameter sets of the PDF. Finally, the PDFs of the estimation error are reconstructed based on the clustering results, thereby designing fault diagnosis strategies that enable a rapid and highly accurate diagnosis of early-stage faults. Simulation results demonstrate that the proposed strategies achieved an early fault diagnosis accuracy rate of over 90%, with the fastest diagnosis time being approximately 0.11 s. Under the same fault conditions, the diagnosis time is 1 s faster than that of a k-means-based fault diagnosis strategy. This study provides a threshold-free, high-accuracy, and rapid fault diagnosis strategy for early fault diagnosis in WECS. By combining neural networks, the proposed method addresses the issue of threshold dependency in fault diagnosis, with potential applications in improving the reliability and safety of wind power generation. Full article

Faults in the DC drive circuit of UAV electro-hydrostatic actuators directly affect the flight safety of a UAV. An integrated learning and Bayesian network-based fault diagnosis strategy is proposed to address the problems of early fault diagnosis, poor unbalanced data processing performance, and lack of interpretability in intelligent fault diagnosis in engineering practice. In the data preprocessing stage, Pearson coefficients are used for feature correlation analysis, and XGBoost performs feature screening to extract key features from the collected DC drive circuit data. This process effectively saves computational resources while significantly reducing the risk of overfitting. The optimal weak learner selection for the high-performance boosting integrated learner is identified through comparative validation. The performance of the proposed diagnostic strategy is fully verified by setting up different comparison algorithms in two experimental circuits. The experimental results show that the strategy outperforms the comparison algorithms in various scenarios such as data balancing, data imbalance, early-stage faults, and high noise; in particular, it shows a significant advantage in diagnosing data imbalance and early-stage faults. The interpretable fault diagnosis of UAV DC drive circuits is realized by the interpretation strategy of Bayesian networks, which provides the necessary theoretical and methodological support for practical engineering operations. Full article

Fault detection and diagnosis (FDD) methods and fault-tolerant control (FTC) have been the focus of intensive research across various fields to ensure safe operation, reduce costs, and optimize maintenance tasks. Unmanned aerial vehicles (UAVs), particularly quadcopters or quadrotors, are often prone to faults in sensors and actuators due to their complex dynamics and exposure to various external uncertainties. In this context, this work implements different FDD approaches based on the Kalman filter (KF) for fault estimation to achieve FTC of the quadcopter, considering different faults with nonlinear behaviors and the possibility of simultaneous occurrences in actuators and sensors. Three KF approaches are considered in the analysis: linear KF, extended KF (EKF), and unscented KF (UKF), along with three-stage and adaptive variations of the KF. FDD methods, especially the adaptive filter, could enhance fault estimation performance in the scenarios considered. This led to a significant improvement in the safety and reliability of the quadcopter through the FTC architecture, as the system, which previously became unstable in the presence of faults, could maintain stable operation when subjected to uncertainties. Full article

Bearings are the most vulnerable component in low-voltage induction motors from a maintenance standpoint. Vibration monitoring is the benchmark technique for identifying mechanical faults in rotating machinery, including the diagnosis of bearing defects. The study of different bearing fault phenomena under induction motor transient conditions offers interesting capabilities to enhance classic fault detection techniques. This study analyzes the low-frequency localized bearing fault signatures in both the inner and outer races during the start-up and steady-state operation of inverter-fed and line-started induction motors. For this aim, the classic vibration envelope spectrum technique is explored in the time–frequency domain by using a simple, resampling-free, Short Time Fourier Transform (STFT) and a band-pass filtering stage. The vibration data are acquired in the motor housing in the radial direction for different load points. In addition, two different localized defect sizes are considered to explore the influence of the defect width. The analysis of extracted low-frequency characteristic frequencies conducted in this study demonstrates the feasibility of detecting early-stage localized bearing defects in induction motors across various operating conditions and actuation modes. Full article

The first quarter of the 21st century has witnessed many technological innovations in various sectors. Likewise, the COVID-19 pandemic triggered the acceleration of digital transformation in organizations driven by artificial intelligence and communication technologies in Industry 4.0 and Industry 5.0. Aiming at the construction of digital twins, virtual representations of a physical system allow real-time bidirectional communication. This will allow the monitoring of operations, identification of possible failures, and decision making based on technical evidence. In this study, a fault diagnosis solution is proposed, based on the construction of a digital twin, for a cloud-based Industrial Internet of Things (IIoT) system contemplating the control of electro-hydrostatic actuators (EHAs). The system was supported by a deep learning model using Long Short-Term Memory (LSTM) networks for an effective diagnostic approach. The implemented study considers data preparation and integration and system development and application to evaluate the performance against the fault diagnosis problem. According to the results obtained, positive results are shown in the construction of the digital twin using a deep learning model for the fault diagnosis problem of an active EHA-IIoT configuration. Full article

The task of automatically and intelligently diagnosing faults in marine equipment is of great significance due to the numerous duties that shipboard professionals must handle. Incorporating automated and intelligent systems on ships allows for more efficient equipment monitoring and better decision-making. This approach has attracted considerable interest in both academia and industry because of its potential for economic savings and improved safety. Several fault diagnosis methods are documented in the literature, often involving mathematical and control theory models. However, due to the inherent complexity of some processes, not all characteristics are precisely known, making mathematical modeling highly challenging. As a result, fault diagnosis often depends on data or heuristic information. Fuzzy logic theory is particularly well suited for processing this type of information. Therefore, this paper employs fuzzy models to diagnose faults in a marine pneumatic servo-actuated valve. The fuzzy models used in fault diagnosis are obtained from the data. These fuzzy models are identified for the normal operation of the marine pneumatic servo-actuated valve, and for each fault, predicting the system’s outputs from the inputs and outputs of the process. The proposed fault diagnosis framework analyzes the discrepancy signals between the outputs of the fuzzy models and the actual process outputs. These discrepancies, known as residuals, help in detecting and isolating equipment faults. The fault isolation process uses an intelligent decision-making approach to determine the specific fault in the system. This method is applied to diagnose abrupt faults in a marine pneumatic servo-actuated valve. The approach presented was used to detect and diagnose three very important faults in the operation of a marine pneumatic servo-actuated valve. The three faults were correctly detected and isolated, and no errors were detected in this detection and isolation process. Full article

With the continuous development of actuator technology, the Electro-Mechanical Actuator (EMA) is gradually becoming the first choice in the aviation field. Permanent Magnet Synchronous Motor (PMSM) is one of the core components of EMA, and its healthy state determines the working performance of EMA. In this paper, the interturn short-circuit fault of PMSM is taken as the typical fault, and a new fault diagnosis framework is proposed based on a wide-kernel convolutional neural network (WCNN) and few-shot learning. Firstly, the wide convolution kernel is added as the first layer to extract short-time features while automatically learning deeply oriented features oriented to diagnosis and removing useless features. Then, the twin neural network is introduced to establish a wide kernel convolutional neural network, which can also achieve good diagnostic results under a few-shot learning framework. The effectiveness of the proposed method is verified by the general data set. The results show that the accuracy of few-shot learning is 9% higher than that of WCNN when the fault data are small. Finally, a fault test platform was built for EMA to collect three-phase current data under different fault states, and the collected data were used to complete the fault diagnosis of PMSM. With limited data, the accuracy of few-shot learning increased by 8% on average compared with WCNN, which has good engineering value. Full article

This paper presents a concomitant observer-based multi-level fault-tolerant control (FTC) for near-space vehicles (NSVs) with a new type dissimilar redundant actuation system (NT-DRAS). When NSV flight control system faults occur in NT-DRAS and attitude-corresponding sensors, the NSV hybrid output states, including the concomitant observer usable states and the real system states, are applied to solve the FTC gain by using the linear quadratic regulator (LQR) technique. Furthermore, since NT-DRAS is used in NSVs, a multi-level (actuation system level and flight control level) FTC strategy integrating NT-DRAS channel switching and flight control LQR is proposed for complex and worsening fault cases. The most important finding is that though the proposed strategy is applicable for worsening fault cases in NSVs, systematic and accurate criteria for the process being performed are necessary and can improve the FTC efficiency with minimal FTC resources. Additionally, such criteria can improve the NSV’s responsiveness to comprehensive faults, provided that the real-time performance of the fault detection and diagnosis (FDD) scheme can be further optimized. The concomitant observer convergence and the multi-level FTC strategy have been verified by numerical simulations based on the Matlab/Simulink platform. Full article

An aeroengine faces multi-source uncertainty consisting of aeroengine epistemic uncertainty and the control system stochastic uncertainty during operation. This paper investigates actuator fault estimation under multi-source uncertainty to enhance the fault diagnosis capability of aero-engine control systems in complex environments. With the polynomial chaos expansion-based discrete stochastic model quantification, the optimal filter under multi-source uncertainty, the Hyperelliptic Kalman Filter, is proposed. Meanwhile, by treating actuator fault as unknown input, the Two-stage Hyperelliptic Kalman Filter (TSHeKF) is also proposed to achieve optimal fault estimation under multi-source uncertainty. However, considering that the biases of the model are often fixed for the individual, the TSHeKF-based fault estimation is robust and leads to inevitable conservativeness. By adding the additional estimation of the unknown deviation in state function caused by probabilistic system parameters, the hybrid fault observer (HFO) is proposed based on the TSHeKF and realizes conservativeness-reduced estimation for actuator fault under multi-source uncertainty. Numerical simulations show the effectiveness and optimality of the proposed HFO in state estimation, output prediction, and fault estimation for both single and multi-fault modes, when considering multi-source uncertainty. Furthermore, Monte Carlo experiments have demonstrated that the HFO-based optimal fault estimation is less conservative and more accurate than the Two-stage Kalman Filter and TSHeKF, providing better safety and more reliable aeroengine operation assurance. Full article

Model-based fault diagnosis serves as a powerful technique for addressing fault detection and isolation issues in control systems. However, diagnosing faults in closed-loop control systems is more challenging due to their inherent robustness. This paper aims to detect and isolate actuator and sensor faults in the cascade electro-hydraulic control system of a turbofan engine. Based on the fault characteristics, we design a robust unknown perturbation decoupling residual generator and an optimal fault observer specifically for the inner and outer control loops to detect potential faults. To locate the faults, we analyze the steady-state propagation laws of actuator and sensor faults within the loops using the final value theorem. Based on this, we establish the minimal-dimensional fault influence distribution matrix specific to the cascade turbofan engine control system. Subsequently, we construct the normalized residual vectors and monitor its vector angles against each row of the fault influence distribution matrix to isolate faults. Experiments conducted on an electro-hydraulic test bench demonstrate that our proposed method can accurately locate four typical faults of actuators and sensors within the cascade electro-hydraulic control system. This study enriches the existing fault isolation methods for complex dynamic systems and lays the foundation for guiding component repair and maintenance. Full article