Multiple-fault diagnosis for spacecraft attitude control systems using RBFNN-based observers

Guo, Xiang-Gui; Tian, Meng; Li, Qing; Ahn, Choon Ki; Yang, Yan

doi:10.1016/j.ast.2020.106195

Cited by 40 publications

(10 citation statements)

References 39 publications

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“…Guo, X. G et al [25] discussed the face of external disturbances and nonlinear uncertainties and provided a novel Multiple-Fault Diagnosis (MFD) approach for a spacecraft attitude control system that makes use of observers based on Radial Basis Function Neural Networks (RBFNNs). The last step is to construct a series of RBFNN in fault isolation observers to isolate the malfunctions of individual parts.…”

Section: Surveymentioning

confidence: 99%

Fault diagnosis of control system based on neural network data fusion

Li¹,

Huang²,

Hou³

2022

Preprint

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Induction motors are the primary driving power in modern manufacturing, and fault diagnosis is critical to product safety, component quality, and maintenance cost management. The fundamental principle behind fault diagnosis is establishing the problem's kind, size, location and time of discovery based on the system's available data. Fault diagnosis has evolved into a critical component of contemporary process automation. It lays the groundwork for fault tolerance, dependability, and security, essential design aspects in complex engineering systems. This study provides solutions to these problems by introducing a Convolutional Neural Network with several tasks (CNN) with data coimbaining for defect identification and regionalization in rolling element bearings. Predictive maintenance of mechanical equipment utilizing multi-source sensing information from the Internet of Things (IoT) with fusion data processing technologies may greatly increase machine service life and save labour costs for identifying mechanical issues, making it a very important topic. IoT-CNN data fusion may balance the pace of convergence of two classification tasks, allowing training procedures to perform fault diagnosis and localization concurrently, identifying a defect utilizing symptoms, knowledge application, and test results in analysis. The findings indicate discovery and exploration developing a better smart integration model that integrates advantages of multiple fused models is challenging and has certain values for accelerating the improvement of mechanical defect detection and forecast.

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Section: Surveymentioning

confidence: 99%

Fault diagnosis of control system based on neural network data fusion

Li¹,

Huang²,

Hou³

2022

Preprint

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“…A sparsogram tool was developed to help choose the right filtering band for envelope analysis. Reference [23] proposed a fault diagnosis method based on an RBFNN. A series of fault isolation observers based on an RBFNN were designed to completely decouple different faults.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network

2023

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The hydraulic heightening system is the core component of the shearer, and its stable operation directly affects the safety and reliability of the equipment, so it is of great significance to realize an efficient and accurate fault diagnosis. This paper proposes a fault diagnosis method combining a rough set and radial basis function neural network (RS-RBFNN). Firstly, the RS is used to discretize the original fault data set and attribute reduction, remove the redundant information, and mine the implicit knowledge and potential rules. Then, the topology structure of the RBFNN is determined. The mapping relationship is established between the fault symptom and category. The fault diagnosis is carried out with Python language. Finally, the method is compared with two diagnostic methods including a back propagation neural network (BPNN) and RBFNN. The research results show that the RS-RBFNN has the highest fault diagnosis accuracy, with an average of 98.68%, which verifies the effectiveness of the proposed fault diagnosis method.

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“…However, various failures during the operation of the actual system can reduce its reliability and destroy the stability of the system, even causing serious safety accidents. Therefore, to improve the reliability and safety of control systems, fault-diagnosis technology has attracted great attention from scholars and numerous research results have been achieved in the fields of aerospace and industrial processes since the 1970s (Guo et al, 2020; Li et al, 2022; Xiao et al, 2020; Yuan et al, 2020; Zhou et al, 2021). In the field of practical engineering, faults are very common.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fast smooth super-twisting observer–based robust fault diagnosis for nonlinear systems

Gao

Zhang

2023

Transactions of the Institute of Measurement and Control

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This study investigates the problem of robust fault diagnosis for nonlinear systems subjected to actuator faults and unknown input disturbances. A novel adaptive fast smooth super-twisting observer based on the super-twisting and adaptive dual-layer algorithms is presented, and a robust fault-diagnosis scheme based on an adaptive threshold is proposed to take the estimation error generated by the observer as the residual. In the adaptive fast smooth super-twisting observer, additional fractional powers less than 1 and linear terms are added to improve the smoothness and rapidity of the observer. The finite-time stability of the adaptive fast smooth super-twisting observer is then analyzed based on the Moreno–Lyapunov function algorithm, and rigorous proof confirms that the estimation-error dynamic is robust to unknown input disturbances and can converge to a region of zero in finite time. Finally, simulations of the rigid spacecraft attitude kinematics and dynamics are used to validate the effectiveness of the developed adaptive fast smooth super-twisting observer and the designed robust fault-diagnosis scheme.

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Multiple-fault diagnosis for spacecraft attitude control systems using RBFNN-based observers

Cited by 40 publications

References 39 publications

Fault diagnosis of control system based on neural network data fusion

Fault diagnosis of control system based on neural network data fusion

A Fault Diagnosis Method of the Shearer Hydraulic Heightening System Based on a Rough Set and RBF Neural Network

Adaptive fast smooth super-twisting observer–based robust fault diagnosis for nonlinear systems

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