Figure 4 - uploaded by Pengqian Han
Content may be subject to copyright.
Source publication
This article proposes a fault diagnosis method for closed-loop satellite attitude control systems based on a fuzzy model and parity equation. The fault in a closed-loop system is propagated with the feedback loop, increasing the difficulty of fault diagnosis and isolation. The study uses a Takagi-Sugeno (T-S) fuzzy model and parity equation to diag...
Contexts in source publication
Context 1
... estimation results for a time-varying fault are shown in Figure 14. Figure 14 shows that based on the fault isolation, the reduced dimension of the FDPV can be used to estimate the parameter of a time-varying fault. ...
Context 2
... estimation results for a time-varying fault are shown in Figure 14. Figure 14 shows that based on the fault isolation, the reduced dimension of the FDPV can be used to estimate the parameter of a time-varying fault. ...
Context 3
... estimation results for a time-varying fault are shown in Figure 14. Figure 14 shows that based on the fault isolation, the reduced dimension of the FDPV can be used to estimate the parameter of a time-varying fault. ...
Similar publications
An adaptive unknown input observer for nonlinear systems whose nonlinear functions
satisfy Lipschitz condition is presented. The main advantage of the proposed observer is that it
is sufficient to satisfy the standard existence conditions of normal unknown input observer
design. Another advantage is that it estimates both states and parameters simu...
Symmetries play very important in the dynamics of robot systems. The relevant control of robot arm motion with fault diagnosis including the optimized fuzzy algorithm based on the error rate adjustment P, I, D value (Fuzzy PID algorithm) model relies on symmetry principles. A robot is a kind of mechanical device that can program and perform certain...
This study investigates the problem of fault diagnosis observer design in the finite frequency domain for discrete‐time Lipschitz nonlinear systems simultaneously subjected to actuator and sensor faults and unknown input disturbances. An augmented descriptor system is developed by constructing an augmented state consisting of system states and sens...
Incidents in extreme environments can bring unprecedented consequences that would endanger the lives of many humans. Real‐time and early detection of cracks and defects in the steel used for the construction of industrial sites can bring many benefits to remote operators and site managers. Although different methods and systems have been proposed i...
Citations
... Over the past few decades, fault diagnosis of control systems has been widely investigated in various research fields, yielding numerous results, for example, in aerospace systems [7,8], wind energy conversion systems [9,10], chemical processes [11], and robotic systems [12]. The existing model-based fault diagnosis methods mainly consist of observer-based [13], parameter identificationbased [14], and parity space-based [15,16] approaches, most of which are designed based on the first principle model of open-loop system parameterization without considering the impact of feedback control on the diagnostic system. However, it was found in [17] that there is a trade-off between controller robustness and the sensitivity of the detection filter in closed-loop control systems. 2 According to [18], a numerical example of a closed-loop three-tank system was used to demonstrate the inability of open-loop fault diagnosis approaches to detect system faults in the proposed closedloop system. ...
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.
... Model-based diagnostic methods require accurate system models to be known. It is generally divided into the parameter estimation method [5], the state prediction method [6], and the parity space method [7]. For hydraulic systems, the basic idea is to combine theoretical modeling and parameter identification and determine faults based on the deviation between parameter estimates and normal values. ...
Hydraulic multi-way valves as core components are widely applied in engineering machinery, mining machinery, and metallurgical industries. Due to the harsh working environment, faults in hydraulic multi-way valves are prone to occur, and the faults that occur are hidden. Moreover, hydraulic multi-way valves are expensive, and multiple experiments are difficult to replicate to obtain true fault data. Therefore, it is not easy to achieve fault diagnosis of hydraulic multi-way valves. To address this problem, an effective intelligent fault diagnosis method is proposed using an improved Squeeze-Excitation Convolution Neural Network and Gated Recurrent Unit (SECNN-GRU). The effectiveness of the method is verified by designing a simulation model for a hydraulic multi-way valve to generate fault data, as well as the actual data obtained by establishing an experimental platform for a directional valve. In this method, shallow statistical features are first extracted from data containing fault information, and then fault features with high correlation with fault types are selected using the Maximum Relevance Minimum Redundancy algorithm (mRMR). Next, spatial dimension features are extracted through CNN. By adding the Squeeze-Excitation Block, different weights are assigned to features to obtain weighted feature vectors. Finally, the time-dimension features of the weighted feature vectors are extracted and fused through GRU, and the fused features are classified using a classifier. The fault data obtained from the simulation model verifies that the average diagnostic accuracy of this method can reach 98.94%. The average accuracy of this method can reach 92.10% (A1 sensor as an example) through experimental data validation of the directional valve. Compared with other intelligent diagnostic algorithms, the proposed method has better stationarity and higher diagnostic accuracy, providing a feasible solution for fault diagnosis of the hydraulic multi-way valve.
... Earlier literature [24] gave methods to obtain parity relations using the system analytical model. Recent studies on fault diagnosis methods based on parity space can be found in the literature [98] [123]. The literature [123] extended the parity space approach from linear systems to nonlinear systems which are described by TS fuzzy models. ...
... The literature [123] extended the parity space approach from linear systems to nonlinear systems which are described by TS fuzzy models. The literature [98] performed diagnosis for actuator faults and sensor faults in satellite altitude control systems by combining the TS fuzzy model with the parity space method. ...
... The monitoring and diagnosis of a chemical process is crucially important to ensure the safety and the quality of the product. [1][2][3][4][5][6][7] With the rapid development of the modern industries, a large amount of data emerges and a new challenge to multivariate statistics process monitoring (MSPM) is given. In the area of MSPM, some approaches have been reported. ...
Multivariate statistics process monitoring can achieve dimensionality reduction and latent feature extraction on process variables. However, process variables without beneficial information may affect the monitoring performance. This article proposes a distributed principal component analysis method based on the angle-relevant variable selection for plant-wide process monitoring. The directions of principal components are utilized to construct the sub-blocks, where the variables in each sub-block are determined by angle. After establishing the principal component analysis model in each sub-block, the monitoring results are fused by Bayesian inference. The simulation results show that the proposed method can select the responsible variables effectively and enhance the monitoring performance.
With the rising number of satellite launches every year, safety and reliability of these systems becomes an issue of paramount importance. In this work, we present a combined approach for attitude control and fault detection in a satellite actuated with reaction wheels. For attitude control we use a Lyapunov based proportional-derivative control law using quaternion feedback, while the fault detection method is based on a non-linear observer. The observer predicts the state of the satellite using system dynamics and the given control inputs. In the absence of any faults, the estimated state matches the measured state closely. On the other hand, a fault is said to have occurred if the difference between the predicted state and the measured state (the residual) is above a set threshold. We perform simulations where we test the system using fault signals. We find that the proposed approach can control the satellite attitude as well as detect the presence of faults accurately. We envisage that this approach can be used to design safer and more reliable space systems.
Fault diagnosis for satellite attitude control systems is practically meaningful for improving the safety and reliability of satellites. In observer-based robust FD, one major concern is to answer if the choice of a threshold satisfies an acceptable trade-off between FAR and FDR. Besides, knowledge of fault isolability is useful for answering how difficult it is to isolate a fault from another one. With these in mind, the design and performance analysis of an -optimal fault diagnosis system are performed in this chapter for satellite attitude control systems, which concerns with fault detectability and fault isolability.
In this paper, the problem of closed-loop intermittent sensor fault (ISF) detection is investigated for a class of linear stochastic time-delay systems with unknown disturbances. A modified unknown input observer (UIO) is proposed to eliminate the influence of delayed errors caused by the discrete-time proportional-integral controller and constant time delay. In order to detect the appearing time and disappearing time of the ISF, a truncated residual is designed by introducing a sliding-time window. Moreover, two hypothesis tests are utilized to set the ISF detection thresholds, and the detectability, false alarm rates as well as missing alarm rates of the ISF are analyzed in the framework of statistical analysis. Finally, the effectiveness of the proposed method is validated via a simulation example of a simplified radial flight control system.
Because the hydraulic directional valve usually works in a bad working environment and is disturbed by multi-factor noise, the traditional single sensor monitoring technology is difficult to use for an accurate diagnosis of it. Therefore, a fault diagnosis method based on multi-sensor information fusion is proposed in this paper to reduce the inaccuracy and uncertainty of traditional single sensor information diagnosis technology and to realize accurate monitoring for the location or diagnosis of early faults in such valves in noisy environments. Firstly, the statistical features of signals collected by the multi-sensor are extracted and the depth features are obtained by a convolutional neural network (CNN) to form a complete and stable multi-dimensional feature set. Secondly, to obtain a weighted multi-dimensional feature set, the multi-dimensional feature sets of similar sensors are combined, and the entropy weight method is used to weight these features to reduce the interference of insensitive features. Finally, the attention mechanism is introduced to improve the dual-channel CNN, which is used to adaptively fuse the weighted multi-dimensional feature sets of heterogeneous sensors, to flexibly select heterogeneous sensor information so as to achieve an accurate diagnosis. Experimental results show that the weighted multi-dimensional feature set obtained by the proposed method has a high fault-representation ability and low information redundancy. It can diagnose simultaneously internal wear faults of the hydraulic directional valve and electromagnetic faults of actuators that are difficult to diagnose by traditional methods. This proposed method can achieve high fault-diagnosis accuracy under severe working conditions.
