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The aircraft control system controls the whole flight movement process. Its fault detection can assist the aircraft PHM system in making decisions and completing the targeted maintenance, which is of great significance to improve the safety and reliability of the aircraft. In this paper, by taking advantage of the strong leaning and intelligent rec...
Citations
... Destabilising effects on the system [1], [2]. Considering the aircraft as a control object, the control signal is properly calculated depending on the diagnosis of abnormal events such as damage or excessive error in a certain component of the system [3], [4]. Thus, the diagnostic block, in addition to the task of finding failures and determining the location, type, and form of failures, must also calculate and fix the failures [5]- [8]. ...
This article presents the problem of designing an automatic control system that is stable against errors and failures of sensors on aircraft. The sensor system has a technical diagnostic block that ensures diagnosis and eliminates typical errors and failures. Based on the determination of the error vector, damage can occur by adding measurement elements corresponding to the measurement parameters to the control system. When there are errors or failures of the sensor elements, the state vector of the system changes and is determined by measurements. The difference between the measured vector components when there are errors, failures and when working normally is the basis of the working algorithm of the failure diagnosis block. The results demonstrate encouraging prospects for practical implementations.
... When there is a vulnerability in the system, or when there is no clear definition of insecure access, then the vulnerability poses a threat to the system, so a different detection method must be proposed to enhance network intrusion detection [5][6]. Network intrusion detection system can dynamically detect various accesses and identify the attacking accesses in it to enhance the security of the network; this method can compensate for the inability of traditional methods to detect internal behavior and can further enhance the network security and provide protection to the network security model [7][8]. ...
Network intrusion detection has been widely discussed and studied as an important part of protecting network security. Therefore, this paper presents an in-depth study of the application of an improved V-detector algorithm in network intrusion detection. In this paper, we construct a V-detector intrusion detection model, adopt the “self-oriented” identification principle, and randomly generate detectors with large differences from the health library. A smaller number of detectors are used to compare the data information generated by the computer, and if they are similar, they are judged as intrusions. Intrusion detection experiments are performed on multiple types of networks by using classifiers to determine whether the access to be detected is an attack access. The experimental results show that the model has the lowest false alarm rate for mixed feature networks, with a false alarm rate of only 13% and a detection rate of 89%, with a sample size of 25,987. After the improvement of the V-detector intrusion detection model, the error correction output problem leads to a network intrusion with a miss rate of only 11% and a protection rate of 85%. The experimental data proved that the model has the advantages of large data size and comprehensive intrusion attack types.
... Hypothesis testing is introduced to more effectively evaluate the detector coverage and to optimize the detector coverage region through the application of geometric mathematics in optimizing the detector center location and radius [5]. ...
Negative selection algorithms play an important role in anomaly detection. Interface detectors are a special negative selection algorithm that completely eliminates outer holes, but there are detection blind areas. In this paper, a novel negative selection algorithm with a hypercube interface detector is proposed. It uses self-sample clusters to construct self space, and boundary self-sample clusters to describe the interface detector. It eliminates the detection blind area and improves the detection rate. To validate the performance of the proposed method, experiments were conducted using the iris dataset, the skin segmentation dataset, and the Breast Cancer of Wisconsin (BCW) dataset. Experimental results show that the proposed method in this paper has a higher detection rate, lower false alarm rate, and fewer detectors than other anomaly detection methods for the same parameters.
... In order to display the signal conveniently and efficiently, the rough signal is amplified through experimental study, and the mathematical model of the genetic algorithm sensor coarse signal processing is used to compile the embedded signal recognition, acquisition, and amplification program. Through signal selection, cross-over, and mutation processing, the processing of sensor coarse signal is successfully realized, and a series of signal recognition and enlarged pictures are obtained, which provides a theoretical basis for the research of power signal processing technology [1]. The adaptive genetic algorithm is a dataprocessing method that generates random sequences and then progressively achieves control objectives through the fuzzy control theory. ...
With the development of electronic information science and network transmission technology, signal processing technology is widely used in various fields. The processing of sensor coarse signal is the key of signal processing technology, in order to study the signal detection and transmission function of the sensor. A genetic algorithm-based sensor fault signal identification, processing, and detection are proposed, and three common signal analysis and processing methods are summarized. The methods of optimal arrangement of sensors are as follows: effective independent algorithm, genetic algorithm, simulated annealing algorithm, and ant colony algorithm principle are studied in detail; signal analysis methods are as follows: fast Fourier transform, wavelet transform, and HHT transform are studied in detail. In the experimental system of the sensor’s coarse signal processing mode, the optimal arrangement of the measurement points of the acceleration sensor in this experiment is directly related to the information collection effect of the monitoring system. Combined with numerical simulation and engineering cases, the soft computing (genetic algorithm, simulated annealing algorithm, and ant colony algorithm) is analyzed in detail, out of the MATLAB program for soft computing. Taking four typical functions as the numerical experimental platform, the three algorithms are used for comparative experimental analysis, and their optimized performance and application range are analyzed. Finally, the practical application performance of soft computing is tested by the practical application problem of optimal path optimization of measuring points. When there are only 10 measuring points, all three algorithms can quickly converge to the global optimal solution, but when there are 100 measuring points, only approximate solutions can be obtained.
... The work of Dasgupta [42] proved NSA could work to detect the fault in the aircraft control system. Another aircraft based work [142] also did similar work, but as they did not state the prior research of [42], their contributions are questionable in terms of novelty. ...
The Negative selection Algorithm (NSA) is one of the important methods in the field of Immunological Computation (or Artificial Immune Systems). Over the years, some progress was made which turns this algorithm (NSA) into an efficient approach to solve problems in different domain. This review takes into account these signs of progress during the last decade and categorizes those based on different characteristics and performances. Our study shows that NSAs evolution can be labeled in four ways highlighting the most notable NSA variations and their limitations in different application domains. We also present alternative approaches to NSA for comparison and analysis. It is evident that NSA performs better for nonlinear representation than most of the other methods, and it can outperform neural-based models in computation time. We summarize NSAs development and highlight challenges in NSA research in comparison with other similar models.
... The negative selection algorithm is a fault diagnosis method proposed by Forrest et al. through learning the human immune system [7] .The normal self set is used to generate the detector set randomly for fault detection, which does not need prior knowledge and has strong robustness [8]. Extenics is a discipline founded by researcher Cai Wen, in which matter element, correlation function and extension set are the basis of extenics. ...
In order to solve the problem that is difficult to extract the fault data of bearing of pumping unit in operation, a negative selection algorithm based on extension theory is proposed to detect the abnormal of bearing. First, a negative selection algorithm detector set model is constructed by using matter element. Synthetic correlation degree function is used as the matching rule of detector generation stage and bearing fault anomaly detection stage. Genetic Particle Swarm Optimization (GPSO) is used to generate detector set. Aiming at the problem of large redundancy of the detector set, the correlation function is used to formulate merging rules to merge the detector set interval. Finally, bearing anomaly detection is carried out by using the bearing fault data of Case Western Reserve University. The results show that the activation rate of the inner ring fault detector is 98.89%, the activation rate of the outer ring fault detector is 98.61%, and the activation rate of the ball fault detector is 99.17%. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
... The work of Dasgupta [37] proved NSA could work to detect the fault in the aircraft control system. Another aircraft based work [23] also did similar work, but as they did not state the prior research of [37], their contributions are questionable in terms of novelty. ...
The Negative selection Algorithm (NSA) is one of the important methods in the field of Immunological Computation (or Artificial Immune Systems). Over the years, some progress was made which turns this algorithm (NSA) into an efficient approach to solve problems in different domain. This review takes into account these signs of progress during the last decade and categorizes those based on different characteristics and performances. Our study shows that NSA's evolution can be labeled in four ways highlighting the most notable NSA variations and their limitations in different application domains. We also present alternative approaches to NSA for comparison and analysis. It is evident that NSA performs better for nonlinear representation than most of the other methods, and it can outperform neural-based models in computation time. We summarize NSA's development and highlight challenges in NSA research in comparison with other similar models.
This paper proposes a novel hybrid health monitoring approach to monitor the system-level degradation of aircraft flight control systems (FCSs). The idea derives from observing the nonlinear hysteresis phenomenon in FCS. First, a health FCS model is developed to implement the nonlinear degradation signal extraction of FCS by building an adaptive-network-based fuzzy inference system, a data-driven method. Subsequently, the Jump Markov autoregressive exogenous (JMARX) system with time delays is adopted to establish the FCS system-level degradation model. An expectation maximum-convex optimization algorithm is innovatively proposed to identify the model parameters. After that, three health indicators associated with the degradation model parameters are utilized for FCS system-level health monitoring. Finally, a practical flight experiment is conducted by a civil aircraft. The obtained experimental data is used to validate the effectiveness of the proposed FCS monitoring approach. The model of the time-delay JMARX system gets good modeling evaluation results on mean absolute error, standard deviation, etc. Besides, each of the three health indicators shows a clear FCS degradation tendency, which indicates that the proposed method successfully extracts the degradation information and monitors the health states of FCS. This approach is potential for practical and effective engineering applications in the aviation industry.
