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9th IEEE International Conference on Computer Science and Education (ICCSE)

作者： Khan, M. Tahir Hussain, S. Bakhtair, S. Khan, A. Zeb Javed, S. Iqbal, J. Univ Engn & Technol Peshawar Inst Mechatron Engn Peshawar Pakistan Natl Univ Sci & Technol Coll Elect & Mech Engn Islamabad Pakistan

ISBN: (纸本)9781479929511

This paper describes the methodology for fault detection in autonomous mobile robots. In this, artificial immune system based approach;a set of fault detectors is generated using negative selection algorithm which is responsible for detecting faulty behavior of system. The idea is based on the self-non self-discrimination observed in biological immune system. The feasibility of scheme is implemented on sensors of simulated mobile robot.

关键词： negative selection algorithm fault detection Artificial Immune System

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A negative selection algorithm applied to helicopter actuator fault detection

A negative selection algorithm applied to helicopter actuato...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Rakotomamonjy, Thomas Le Boulanger, Morgane Off Natl Etud & Rech Aerosp French Aerosp Lab FR-13300 Salon De Provence France Ecole Air FR-13300 Salon De Provence France

ISBN: (纸本)9781479967735

In order to detect the failure of an actuator of a helicopter swashplate, a simple detection method based on a negative selection algorithm (NSA) is developed. This technique, inspired by existing rules found in the biological immunity, rely on a set of elemental detectors which have been trained in order to recognize only the non-self (i.e. faulty) behaviors of the system, without needing an explicit model of the system dynamics. A simple model for the actuator failure is developed and coupled with a helicopter flight dynamics model, and some trajectories for the algorithm training and validation are simulated. A procedure for improving the efficiency of the algorithm by introducing weighting coefficients to the detection function is proposed and successfully tested. Moreover, the performance of the algorithm is slightly improved by observing the covariance of the flight dynamics states, instead of the variables themselves.

关键词： actuators aircraft control failure (mechanical) fault diagnosis helicopters NSA algorithm efficiency improvement algorithm training algorithm validation biological immunity detection function elemental detectors flight dynamics states helicopter actuator fault detection helicopter flight dynamics model helicopter swashplate actuator failure detection negative selection algorithm nonself faulty system behavior recognition weighting coefficients Actuators Blades Detectors Fault detection Helicopters Rotors Trajectory Helicopters actuators Defect detection Weighting coefficients Aircraft control trajectory Rotors Blades Fault diagnosis negative selection Flight dynamics detector

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Detection of Induction Motors Rotor Faults by Using negative selection algorithm Based on Park's Vector Approach 21

Detection of Induction Motors Rotor Faults by Using Negative...

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International Conference on Electrical Machines (ICEM)

作者： Bilgin, Osman Ogut, Murat Arabaci, Hayri Selcuk Un Fac Engn Dept Elect Elect Engn TR-42075 Konya Turkey Selcuk Un Akoren Vocat Sch Dept Elect & Automat TR-42460 Konya Turkey

ISBN: (纸本)9781479943883

The stator current signals of induction motors are often used to detect the broken rotor bar faults. However, the detection work becomes rather tedious owing to the fact that the fundamental supply frequency is much greater and quite close to the fault frequency. To get rid of this difficulty, the use of filters is often employed. Besides the fundamental supply frequency, the filter methods used tend to press on the fault frequency and necessitate a lot of calculations. Several studies on methods detecting rotor faults exist in the literature. Unlike literature, this study applies the negative selection algorithm on training and testing schemes of the a, b, c stator phase currents subjected to Park transformation after changing them into the id and iq magnitudes. The use of Park transformation eliminates the need of filter designs and lessens calculation load for the algorithm whereas application of the negative selection algorithm helps with detection of both normal and fault values in the system. Backed with the experimental results, the method proposed in this study has shown the effects, accuracy and applicability of the algorithm in detecting broken rotor bar faults in induction motors.

关键词： Broken rotor bar fault detection induction motors artificial immune system negative selection algorithm Park vector

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negative selection algorithm for dengue outbreak detection

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2013年第Sup2期21卷 2345-2356页

作者： Mousavi, Maryam Abu Bakar, Azuraliza Zainudin, Suhaila Awang Long, Zalizah Sahani, Mazrura Vakilian, Mohammadmahdi Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi 43600 Selangor Malaysia Univ Kuala Lumpur Malaysia Inst Informat Technol Kuala Lumpur Malaysia Univ Kebangsaan Malaysia Fac Hlth Sci Bangi 43600 Selangor Malaysia

Dengue is a critical communicable and vector-borne disease and is becoming a serious concern in Malaysia. It is important to have an early detection system that could provide immediate action, such as the control of dengue transmission at a specific location. However, the available strategy and action may give long-term effects to the community since inaccurate decision making or prediction may lead to other circumstances. Moreover, the need to have a system that can detect the outbreak in a reasonable amount of time is critical. In this study, a nature-inspired computing technique, the artificial immune system (AIS), is used for dengue outbreak detection. One of the variants of the AIS algorithms, called the negative selection algorithm (NSA), has been widely applied in anomaly detection and fault detection. This study aims to employ the NSA for dengue outbreak detection.

关键词： Dengue outbreak artificial immune system negative selection algorithm

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Artificial immunity-based energy theft detection for advanced metering infrastructures

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INTERNATIONAL JOURNAL OF CRITICAL INFRASTRUCTURE PROTECTION 2025年 48卷

作者： Fu, Jie Yang, Chengxi Liu, Yuxuan Zhang, Kunsan Li, Jiaqi Li, Beibei State Grid Fujian Elect Power Co Ltd Fuzhou 350003 Fujian Peoples R China Sichuan Univ Sch Cyber Sci & Engn Chengdu 610207 Sichuan Peoples R China

Advanced Metering Infrastructure (AMI) is envisioned to enable smart energy management and consumption while ensuring the integrity of real energy consumption data. However, existing smart meters, gateways, and communication channels are usually weakly protected, often opening a huge door for data eavesdroppers who may be easily to further construct energy thefts. Although some energy theft detection schemes have already been reported in the literature, they often fail to take into account the dense data distribution characteristics of energy consumption data, resulting in compromised detection performance. To this end, we in this paper propose a novel arTificial IM mune based E nergy theft D etection (TIMED) scheme, which can effectively identify five types of energy thefts. Specifically, we first develop an energy consumption data pre-processing method, which can effectively reduce the dimensionality of raw energy consumption data to facilitate the data analyzing efficiency. Second, we design a center-distance-based energy theft detector generation method to create high- quality detectors with low elimination rates. Last, we devise a nonself-based hole repair method for energy theft detectors, which can further reduce the false negative alarms. Extensive experiments on areal public AMI dataset demonstrate that the proposed TIMED scheme is highly effective in identifying pulse attacks, scaling attacks, ramping attacks, random attacks, and smooth-curve attacks. The results show that TIMED outperforms many existing machine learning and traditional artificial immunity-based energy theft detection methods.

关键词： Advanced metering infrastructure (AMI) Energy theft detection Artificial immunity negative selection algorithm

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An integrated approach to detect online financial transaction fraud using negative and clonal selection algorithms with AIS 3

An integrated approach to detect online financial transactio...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Gupta, Rohan Ghanghas, Peshal Uie Chandigarh University Dept. of Ece Mohali India

ISBN: (纸本)9798331515911

The relevancy of fraud detection in account takeover fraud in online financial transactions is at it's peak. The traditional fraud detection approaches have seen a downwards trend. This paper adds more onto the utilization of Biological Immune System to level an AI-based Immune system for detecting frauds and the emerging patterns within it's domain. By implementing various machine learning algorithms such as negative and clonal selection the proposed system can accurately identify previously unknown fraud patterns and various attack strategies used by the unauthorized users and hackers. The integration of this approach seems promising in improving the security and enhancing the dependability of the transactions, hence, safeguarding people from account takeover fraud. © 2025 IEEE.

关键词： Artificial Immune system(AIS) Biological Immune System principles(BISP) clonal selection algorithm Fraud Detection (FD) negative selection algorithm online financial transaction fraud

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Artificial immune system for fault detection and localization in a composite material plate with temperature variation

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2024年第12期46卷 1-14页

作者： de Almeida, Estevao Fuzaro Chavarette, Fabio Roberto Merizio, Igor Feliciani Goncalves, Aparecido Carlos Sao Paulo State Univ Sch Engn Ilha Solteira Dept Mech Engn Ave Brasil56 BR-15385000 Ilha Solteira SP Brazil Sao Paulo State Univ Inst Chem Dept Engn Phys & Math Rua Prof Francisco Degni55 BR-14800060 Araraquara SP Brazil

Modern society is strongly reliant on structural and mechanical systems, and the usage of composite materials in this context has grown dramatically in recent decades. Because of some better qualities over metallic materials, they have been driven by various industry sectors. However, composites often have complex and anisotropic internal structures, which eventually lead to various types of damage, making possible structural failures difficult to diagnose and anticipate. In this context, structural health monitoring (SHM) refers to the process of identifying damage to engineering structures. One of the proposed solutions for SHM is so-called artificial immune systems (AISs), which replicate the human immune system, and this is a field of study that integrates immunology, computer science, and engineering to address complicated computational problems. As a result, the goal of this work is to implement an SHM approach for damage identification and localization based on impedance data from a composite material plate subjected to temperature variations and progressive damage growth. An optimized methodology involving signal analysis in the time domain was achieved through reading and processing based on the label of signals, which presented an F1-score equal to 1.0 and a 100% probability of damage detection is even capable of locating the path in which the damage is inserted. As a result of its average processing speed of 4.8 s and substantial memory capacity, an application for continuous monitoring of composite structures subjected to temperature variations was developed.

关键词： Structural health monitoring Artificial immune system negative selection algorithm Composite materials

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Improved V-detector algorithm based on bagging for earthquake prediction with faults

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JOURNAL OF SUPERCOMPUTING 2024年第16期80卷 24605-24637页

作者： Peng, Lu Liang, Yiwen Yang, He Wuhan Univ Sch Comp Sci 299 Bayi Rd Wuhan 430072 Hubei Peoples R China Wuchang Shouyi Univ Coll Informat Sci & Engn 22 Nanli Rd Wuhan 430064 Hubei Peoples R China Hubei Univ Educ Sch Comp Sci 129 Gaoxin 2nd Rd Wuhan 430205 Hubei Peoples R China

With the highly nonlinear relationship between various seismic feature indicators and earthquakes, the researchers can hardly construct an earthquake model. Meanwhile, the lack of samples for destructive earthquakes also leads to inaccurate medium-to-short-term earthquake magnitude predictions. Therefore, this study proposes a novel model for earthquake prediction, named variant detector bagging algorithm (V-detector-bagging). First, we adopt the geological distribution of earthquakes and faults as a criterion to select the appropriate history catalog data area, and the seismic indicators are calculated through the Gutenberg-Richter laws and Panakkat indicators. Then, we propose the V-detector-bagging algorithm, which combines the V-detector algorithm with the bagging method. The proposed algorithm converts the self-tolerance process into a cycle process, during this process, different selves guide samples to spread widely, generate various detectors covering more wide nonself areas, fill holes that are not covered by detectors in nonself areas, and reduce the false negative rate. Thus, the V-detector-bagging algorithm improves the detection performance of the V-detector. Finally, through experimental validation analysis, the proposed algorithm ranked first in the detection rate on Sichuan and Xinjiang catalog data compared to the popular machine learning methods used in predicting earthquakes and the original V-detector algorithm, with Xinjiang yielding the best results.

关键词： V-detector-bagging algorithm Earthquake prediction Fault zone Artificial immune system negative selection algorithm

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An intelligent manufacturing system based on a recursive control structure

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FRONTIERS IN MECHANICAL ENGINEERING-SWITZERLAND 2025年 10卷

作者： Teng, Bingyan Nanjing Vocat Univ Ind Technol Sch Business & Trade Nanjing Peoples R China

Introduction The excessive uncertainty of in modern manufacturing systems is caused by machine failures, changes in material information, and other factors. In addition, the organizational production mode conflicts brought about by economic and technological development further exacerbate the perception of workshop interference in manufacturing *** In order to further improve the adaptability of manufacturing systems, a control technique based on recursive control structure is proposed, which introduces an immune working mechanism to design the framework network of multi-agent manufacturing systems. Meanwhile, a negative selection algorithm is used to construct an antibody training system that considers perturbation *** The results indicate that immune sensing nodes can effectively monitor manufacturing systems, reducing false alarm rates by over 4%. In the scheduling experiment, the completion time and equipment load improvement rate demonstrated by the research model were 3.29% and 12.38%, respectively. The production balance optimization rate exceeded 90%, far exceeding the results of traditional scheduling schemes, greatly improving the adaptive control capability of manufacturing system *** The regulatory approach proposed in this study can provide reference and assistance for improving the level of industrial production intelligence and establishing a sustainable economic system. However, the research results have not been applied to actual production processes, and the autonomy and coordination of intelligent manufacturing units in actual production processes still need to be further improved. In the future, research models and algorithms will be further explored in this area.

关键词： manufacturing systems recursive structure intelligent agents immune mechanism negative selection algorithm

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Application of negative selection algorithm in the Fault Detection and Diagnosis of LRE

Application of Negative Selection Algorithm in the Fault Det...

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作者： Anbo MING Wei ZHANG Ganyang TIAN Wei ZHENG The High-Tech Institute of Shaan Xi

To overcome the obstacles existed in the fault detection and diagnosis of liquid rocket engine(LRE) such as the lack of real-time, in-time, and veracity, the negative selection algorithm of artificial immune system was introduced. By constructing the artificial recognition ball(ARB) and adopting the maximum similarity rule, the fault detection and diagnosis were carried out. The experiment was performed with steady state data collected in ground test then. In the multi-dimensional modal space, the fault property was discussed further. Faults surround the normal state in different distance. If the parameters deflect more from the values which were supposed, the fault will be more obvious and it will move away from the normal state in the ARB. Some faults are separable, but some are also lapped over partly. Results show that this method has the function of quickly detection, high rate of correction diagnosis, and powerful ability of discovering the unknown fault. It can be widely applied in the state monitoring and fault diagnosis of LRE.

关键词： Liquid Rocket Engine Artificial Immune System Fault Detection and Diagnosis negative selection algorithm Artificial Recognition Ball

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