Comparative Analysis of Real-Time Fault Detection Methods Based on Certain Artificial Intelligent Algorithms for a Hydrogen–Oxygen Rocket Engine

Health monitoring and fault diagnosis of liquid rocket engine (LRE) are the most important concerning issue for the safety of rocket’s flying, especially for the man-carried aerospace engineering. Based on the sensor measurement signals of a certain type of hydrogen-oxygen rocket engine, this paper proposed a real-time fault detection approach using a genetic algorithm-based least squares support vector regression (GA-LSSVR) algorithm for the real-time fault detection of the rocket engine. In order to obtain effective training samples, the data is normalized in this paper. Then, the GA-LSSVR algorithm is derived through comprehensive considerations of the advantages of the Support Vector Regression (SVR) algorithm and Least Square Support Vector Regression (LSSVR). What is more, this paper provided the genetic algorithm to search for the optimal LSSVR parameters. In the end, the computational results of the suggested approach using the rocket practical experimental data are given out. Through the analysis of the results, the effectiveness and the detection accuracy of this presented real-time fault detection method using LSSVR GA-optimized is verified. The experiment results show that this method can effectively diagnose this hydrogen-oxygen rocket engine in real-time, and the method has engineering application value.

The technological advancement in Cyber-Physical Systems (CPS) has evolved into sophisticated hardware, leading to systems that are complex and interconnected. This trend has made modern CPS susceptible to faults. Traditional methods for fault detection and diagnosis are unable to adequately scale up to manage the faults that occur in CPS because of the tight interconnectivity between the physical and cyber parts of CPS. Also, hard real-time constraints present challenges that are not sufficiently addressed by traditional fault-tolerant design approaches; therefore, new methods are needed to deal with these faults. Here, we present a new scheme to detect and diagnose CPS faults, which relies on the combination of knowledge-based and model-driven Fault Detection and Diagnosis (FDD). The method is developed to be applied when building CPS using Discrete Event Methodologies.

A NOVEL MODULAR NONLINEAR NETWORK FOR FAULT DIAGNOSIS AND SUPERVISED PATTERN CLASSIFICATION

This paper discusses fault detection and diagnostics in a solar power plant. After reviewing relevant work, description of a data acquisition system for solar plant monitoring is presented and the list of potential faults to be taken care of is given. Then Bayesian belief network is built to intelligently reason about potential causes of faults detected. Finally, a practical implementation of a computer system to retrieve data, extract relevant information, and reason about fault detection in real time is presented.

Application of advanced fault diagnosis and monitoring techniques allows more efficient, reliable and safe operation of many complex industrial systems. Recently, there has been a significant increase in application of various data-driven deep learning models for motor fault detection and diagnosis problems. Due to high computational complexity and large training dataset requirements of deep learning models, in this study, shallow and adaptive 1D convolutional neural networks (CNNs) are applied to real-time detection and classification of broken rotor bars in induction motors. As opposed to traditional fault diagnosis systems with separately designed feature extraction and classification blocks, the proposed system takes directly raw stator current signals as input and it can automatically learn optimal features with the proper training. The other advantages of the proposed approach are (1) its compact architecture configuration performing only 1D convolutions with a set of filters and subsampling, making it suitable for implementing with real-time circuit monitoring, (2) its requirement for a limited size of training dataset for efficient training of the classifier and (3) its cost-effective implementation. Effectiveness and feasibility of the proposed method is validated by applying it to real motor current data from an induction motor under full load.

Fault diagnosis is critical for safe and reliable operations of quadrotor unmanned aerial vehicles (UAVs), so that timely remedial measures can be taken to reduce negative impacts of faults. This paper presents a real-time actuator fault detection and isolation (FDI) method for quadrotor UAV flight process. First, a linear parameter-varying model is established to describe quadrotor UAV dynamics, which considers measurement noises and external disturbances. Then, an FDI strategy is developed based on directional residuals, in which only one observer-based residual generator is required and H − / L ∞ indices are incorporated to balance fault sensitivity and disturbance robustness. A threshold is derived from the L ∞ performance condition, and actuator faults are detected through residual evaluation. Furthermore, by introducing fault feature vectors associated with the system model, the faulty actuator is isolated promptly by analyzing directional correlations between generated residual vectors and defined fault feature vectors. Finally, based on a quadrotor UAV platform, the effectiveness and superiority of the proposed FDI method are verified through online trajectory tracking processes.

Electrolytic capacitor surface defect detection based on deep convolution neural network

This paper presents a new scheme of adaptive vector control for inverter-fed induction motors, which can identify crucial motor parameters in a real-time manner. The total system has a novel configuration consisting of two subsystems, a vector control system in the synchronous frame and an adaptive identification system in the stationary frame. The proposed adaptive identification system is new and can adaptively and simultaneously identify two motor parameters varying dynamically with temperature, magnetic saturation or motor faults, which are related directly with control performance and possibly with real-time motor fault detection and diagnosis. High performance of the proposed scheme is verified through practical evaluation tests of the adaptive vector control system. Variation of torque generated by the system to motor frame temperature variation of about 50/spl deg/C is only 0.5%. Linearity of torque generation up to 300% of torque rating is ensured. Deviation from ideal response is almost constant up to 300% of torque rating, whose magnitude is only about 1% of torque rating. Inverse of rotor time constant, which is one of the identified parameters for high control performance, seems to present also straight and valuable information on rotor-bar breaks where rotor resistance increases, inductance decreases and consequently the change of inverse of rotor time constant as their ratio is much enhanced.

Real time fault detection and diagnosis system for onboard engine room

An intelligent measure control system is designed to solve the problem of real-time intelligent detection of thermal defects of high voltage equipment at unmanned substations. This system uses a new method that continuous power of CC2530, a ZigBee wireless single chip, is provided by using monocrystalline silicon solar cells and thermoelectric generation modules. This method can also solve the problem that overheat faults can not be alarmed timely when efficiency of solar cells decrease during the continuous rainy days, and can realize real-time wireless detection and remote fault analysis of thermal defects of high voltage equipment. The supervisory programs of the remote PC and the upper computer are designed through VC + +.NET and the communication mode adopts C/S model based on TCP/IP protocols. The image files are saved as JPEG format by CImage class, which implements the real-time transmission of images. All the forgoing advantages provide a technical assurance to the remote and intelligent control of the heat vice of high-voltage equipment in the unmanned substations.

Accurate fault detection and diagnosis are critical components of any fault-tolerant control system, especially for unmanned aerial vehicles (UAVs) where reliability is paramount. Traditionally, both model-based and data-driven approaches have been applied for fault diagnosis. However, the increasing complexity of high-dimensional UAV systems has shifted focus toward data-driven methods, which leverage advanced classification algorithms to enhance fault identification and isolation. This study builds on this evolution by developing a sophisticated condition-based monitoring (CBM) system specifically designed for multirotor UAVs. In contrast to earlier studies that primarily relied on raw data for classifier training, this work introduces advanced preprocessing techniques and multidomain feature extraction, significantly improving the robustness and accuracy of fault detection. A comparative analysis is performed between feature-selection methods, including recursive feature elimination with cross-validation (RFECV) and variational autoencoder (VAE), to extract critical insights into UAV operational behavior. Through testing and evaluating various classification models on data from a hexarotor UAV under diverse actuator fault conditions, this research identifies optimal approaches for real-time fault detection and diagnosis. Results demonstrate notable improvements across all evaluation metrics, establishing this approach as a substantial advancement in UAV fault tolerance.

Rapid integration of DC-AC inverters in renewable energy systems, such as solar photovoltaics and wind energy, requires robust health monitoring solutions for long-term reliability and fault resilience. Digital Twin (DT) technology has emerged as a transformative tool for real-time monitoring, fault detection, and predictive maintenance of inverters in such systems. This review paper comprehensively presents the state-of-the-art DT approaches implemented for the health monitoring of DC-AC inverters within renewable energy applications. It categorizes methodologies into model-based, data-driven, and hybrid DT models, covering inverter condition monitoring, parameter degradation tracking, and fault diagnosis. The study further outlines optimization and AI techniques, such as Particle Swarm Optimization and neural networks, used in DT development. Key challenges in digital modeling fidelity, thermal and electrical stress estimation, and real-time synchronization are discussed. Finally, the paper identifies future research directions for scalable, edge-AI-integrated DT systems for next-generation renewable energy converters.

With the increasing complexity and frequency of computer network faults, efficient fault diagnosis is crucial to the reliability and security of the network. To this end, this paper proposes an advanced fault diagnosis method based on the improved Teaching Learning-based Optimization (TLBO) algorithm and the Mini Batch Gradient Descent (MBGD) algorithm under the framework of Convolutional Neural Network (CNN). Different from the traditional CNN-based methods, this method innovatively integrates the TLBO algorithm with the Differential Evolution (DE) strategy, optimizes hyper-parameters and training convergence, and significantly improves the detection accuracy and speed. Meanwhile, MBGD can effectively refine the model parameters and prevent convergence to local minima. The experimental results using the public dataset prove the effectiveness of this method, achieving a high classification accuracy rate of up to 88.3% and significantly reducing the false detection rate to below 0.20%. Compared with the traditional CNN model and the latest methods, this method has a faster convergence speed, model stability and fault diagnosis performance. This research provides a robust solution for real-time fault detection and diagnosis, significantly enhancing the reliability and security of complex network systems.

Real-time fault detection and active fault-tolerant control on vehicles can prevent potential accidents and ensure safe autonomous driving. Unlike existing methods which are designed for known faults or require perfect knowledge of vehicle models, this paper proposes a novel data-driven optimal adaptive fault-tolerant control method with model learning for automated vehicles to enable effective and safe driving under various faults. The actuator and sensor faults are integrated in one unified model to describe the vehicle dynamic behavior. The vehicle states are estimated online with only limited onboard sensors in vehicles available. An efficient fault detector using a modified sliding innovation sequence method is developed to ensure accurate fault detection. The dynamic model is adapted online using Gaussian Process based on vehicle running data, accurately representing the dynamic driving behavior. A novel learning-based adaptive fault-tolerant control method is designed to optimize the vehicle movement for autonomous path tracking under various faults with stochastic model predictive control. Simulation experiments validate the effectiveness of the proposed method in fault detection, model online learning, and accurate path tracking across various fault scenarios.

In order to meet the requirements of multi-parameter and real-time fault diagnosis of China VI vehicle emission standards diesel engine air system, this paper focuses on the research of the diesel engine air system fault detection and diagnosis method based on deep learning to improve the operational safety and fault diagnosis efficiency. In this paper, the air system fault detection and diagnosis are completed based on the AE model and the CNN model, combined with the actual operation of the diesel engine. Among them, the AE model successfully detected all real-time operation faults, and the false detection rate on the health data was 0.1162%; the CNN model obtained a 90.77% fault diagnosis accuracy rate on the test set of the real-time operation data set. The results show that the model has high accuracy in the diagnosis of diesel engine faults, which is of great significance to the application of deep learning based on big data processing.