An advanced immune based strategy to obtain an optimal feasible assembly sequence

2016 ◽  
Vol 36 (2) ◽  
pp. 127-137 ◽  
Author(s):  
M.V.A. Raju Bahubalendruni ◽  
B.B.V.L. Deepak ◽  
Bibhuti Bhusan Biswal
Keyword(s):  
Bone Marrow ◽  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Problem Formulation ◽  
Computational Time ◽  
Assembly Sequence ◽  
Artificial Immune ◽  
Content Type ◽  
Assembly Sequences

Purpose The purpose of this study is to develop an intelligent methodology to find out an optimal feasible assembly sequence while considering the assembly predicates. Design/methodology/approach This proposed study is carried out by using two artificial immune system-based models, namely, Bone Marrow Model and Negative Selection Algorithms, to achieve the following objectives: to obtain the possible number of assembly sequences; to obtain the feasible assembly sequences while considering different assembly predicates; and to obtain an optimal feasible assembly sequence. Findings Proposed bone-marrow model determines the possible assembly sequences to ease the intricacy of the problem formulation. Further evaluation has been carried out through negative-selection censoring and monitoring models. These developed models reduce the overall computational time to determine the optimal feasible assembly sequence. Originality/value In this paper, the novel and efficient strategies based on artificial immune system have been developed and proposed to obtain all valid assembly sequences and optimized assembly sequence for a given assembled product using assembly attributes. The introduced methodology has proven its effectiveness in achieving optimal assembly sequence with less computational time.

Influence of assembly predicate consideration on optimal assembly sequence generation

2015 ◽  
Vol 35 (4) ◽  
pp. 309-316 ◽  
Author(s):  
M. V. A. Raju Bahubalendruni ◽  
Bibhuti Bhusan Biswal ◽  
Manish Kumar ◽  
Radharani Nayak
Keyword(s):  
Low Cost ◽  
Problem Formulation ◽  
Search Space ◽  
Computational Time ◽  
Assembly Sequence ◽  
Content Type ◽  
Sequence Generation ◽  
Assembly Sequences ◽  
Assembly Sequence Generation ◽  
Optimal Assembly

Purpose – The purpose of this paper is to find out the significant influence of assembly predicate consideration on optimal assembly sequence generation (ASG) in terms of search space, computational time and possibility of resulting practically not feasible assembly sequences. An appropriate assembly sequence results in minimal lead time and low cost of assembly. ASG is a complex combinatorial optimisation problem which deals with several assembly predicates to result an optimal assembly sequence. The consideration of each assembly predicate highly influences the search space and thereby computational time to achieve valid assembly sequence. Often, the ignoring an assembly predicate leads to inappropriate assembly sequence, which may not be physically possible, sometimes predicate assumption drastic ally raises the search space with high computational time. Design/methodology/approach – The influence of assuming and considering different assembly predicates on optimal assembly sequence generation have been clearly illustrated with examples using part concatenation method. Findings – The presence of physical attachments and type of assembly liaisons decide the consideration of assembly predicate to reduce the complexity of the problem formulation and overall computational time. Originality/value – Most of the times, assembly predicates are ignored to reduce the computational time without considering their impact on the assembly sequence problem irrespective of assembly attributes. The current research proposes direction towards predicate considerations based on the assembly configurations for effective and efficient ASG.

Structural failures diagnosis using a hybrid artificial intelligence method / Diagnóstico de falhas estruturais utilizando um método híbrido de inteligência artificial

2021 ◽  
Vol 7 (7) ◽  
pp. 66873-66893
Author(s):  
Simone Silva Frutuoso de Souza ◽  
Mailon Bruno Pedri de Campos ◽  
Fábio Roberto Chavarette ◽  
Fernando Parra dos Anjos Lima
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Artificial Immune ◽  
Aircraft Structure ◽  
Inspection Process ◽  
Artificial Immune System Algorithm ◽  
Structural Failures ◽  
Aluminum Beam ◽  
Hybrid Artificial Intelligence

This paper presents a Wavelet-artificial immune system algorithm to diagnose failures in aeronautical structures. Basically, after obtaining the vibration signals in the structure, is used the wavelet module for transformed the signals into the wavelet domain. Afterward, a negative selection artificial immune system realizes the diagnosis, identifying and classifying the failures. The main application of this methodology is the auxiliary structures inspection process in order to identify and characterize the flaws, as well as perform the decisions aiming at avoiding accidents or disasters. In order to evaluate this methodology, we carried out the modeling and simulation of signals from a numerical model of an aluminum beam, representing an aircraft structure such as a wing. The results demonstrate the robustness and accuracy methodology.

Partitioned artificial immune system for detection and identification of autonomous flight vehicle abnormal conditions

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ryan Gerald McLaughlin ◽  
Mario G. Perhinschi
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Health Management ◽  
Clustering Methods ◽  
Artificial Immune ◽  
Content Type ◽  
Identification Schemes ◽  
Detection And Identification ◽  
Abnormal Condition ◽  
The Universe

PurposeAn artificial immune system (AIS) for the detection and identification of abnormal operational conditions affecting an unmanned air vehicle (UAV) is developed using the partition of the universe approach. The performance of the proposed methodology is assessed through simulation within the West Virginia University (WVU) unmanned aerial system (UAS) simulation environment.Design/methodology/approachAn AIS is designed and generated for a fixed wing UAV using data from the WVU UAS simulator. A novel partition of the universe approach augmented with the hierarchical multiself strategy is used to define the self, within the AIS paradigm. Several 2-dimensional and 3-dimensional commanded trajectories are simulated under normal and abnormal conditions affecting actuators and sensors. Data recorded are used to build the AIS and develop an abnormal condition detection and identification scheme for the two categories of subsystems. The performance of the methodology is evaluated in terms of detection and identification rates, false alarms and decision times.FindingsThe proposed methodology for UAV abnormal condition detection and identification has the potential to support a comprehensive and integrated solution to the problem of aircraft subsystem health management. The novel partition of the universe approach has been proven to be a promising alternative to the previously investigated clustering methods by providing similar or better performance for the cases investigated.Research limitations/implicationsThe promising results obtained within this research effort motivate further investigation and extension of the proposed methodology toward a complete system health management process, including abnormal condition evaluation and accommodation.Practical implicationsThe use of the partition of the universe approach for AIS generation may potentially represent a valuable alternative to current clustering methods within the AIS paradigm. It can facilitate a simpler and faster implementation of abnormal condition detection and identification schemes.Originality/valueIn this paper, a novel method for AIS generation, the partition of the universe approach, is formulated and applied for the first time for the development of abnormal condition detection and identification schemes for UAVs. This approach is computationally less expensive and mitigates some of the issues related to the typical clustering approaches. The implementation of the proposed approach can potentially enhance the robustness of UAS for safety purposes.

Attack-Resistant aiCAPTCHA Using a Negative Selection Artificial Immune System

2017 ◽  
Author(s):  
Brian M. Powell ◽  
Ekampreet Kalsy ◽  
Gaurav Goswami ◽  
Mayank Vatsa ◽  
Richa Singh ◽  
...  
Keyword(s):  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Artificial Immune

scholarly journals Immunity-based accommodation of aircraft subsystem failures

2017 ◽  
Vol 89 (1) ◽  
pp. 164-175 ◽  
Author(s):  
Adil Togayev ◽  
Mario Perhinschi ◽  
Hever Moncayo ◽  
Dia Al Azzawi ◽  
Andres Perez
Keyword(s):  
Immune System ◽  
Artificial Immune System ◽  
Research Effort ◽  
Time Windows ◽  
Flight Simulation ◽  
Design Development ◽  
Artificial Immune ◽  
Content Type ◽  
Simulation Testing ◽  
System Paradigm

Purpose This paper aims to describe the design, development and flight-simulation testing of an artificial immune-system-based approach for accommodation of different aircraft sub-system failures/damages. Design/methodology/approach The approach is based on building an artificial memory, which represents self- (nominal conditions) and non-self (abnormal conditions) within the artificial immune system paradigm. Self- and non-self are structured as a set of memory cells consisting of measurement strings, over pre-defined time windows. Each string is a set of features values at each sample time of the flight. The accommodation algorithm is based on the cell in the memory that is the most similar to the in-coming measurement. Once the best match is found, control commands corresponding to this match are extracted from the memory and used for control purposes. Findings The results demonstrate the possibility of extracting pilot compensatory commands from the self/non-self structure and capability of the artificial-immune-system-based scheme to accommodate an actuator malfunction, maintain control and complete the task. Research limitations/implications This paper concentrates on investigation of the possibility of extracting compensatory pilot commands. This is a preliminary step toward a more comprehensive solution to the aircraft abnormal condition accommodation problem. Practical implications The results demonstrate the effectiveness of the proposed approach using a motion-based flight simulator for actuator and sensor failures. Originality/value This research effort is focused on investigating the use of the artificial immune system paradigm for control purposes based on a novel methodology.

scholarly journals The Evaluated Measurement of a Combined Genetic Algorithm and Artificial Immune System

2017 ◽  
Vol 7 (4) ◽  
pp. 2071
Author(s):  
Pongsarun Boonyopakorn ◽  
Phayung Meesad
Keyword(s):  
Genetic Algorithm ◽  
Immune System ◽  
Negative Selection ◽  
Artificial Immune System ◽  
Optimization Methods ◽  
The Novel ◽  
Artificial Immune ◽  
Input Variables ◽  
Modified Algorithm ◽  
Better Than

This paper demonstrates a hybrid between two optimization methods which are the Artificial Immune System (AIS) and Genetic Algorithm (GA). The novel algorithm called the immune genetic algorithm (IGA), provides improvement to the results that enable GA and AIS to work separately which is the main objective of this hybrid. Negative selection which is one of the techniques in the AIS, was employed to determine the input variables (populations) of the system. In order to illustrate the effectiveness of the IGA, the comparison with a steady-state GA, AIS, and PSO were also investigated. The testing of the performance was conducted by mathematical testing, problems were divided into single and multiple objectives. The five single objectives were then used to test the modified algorithm, the results showed that IGA performed better than all of the other methods. The DTLZ multiobjective testing functions were then used. The result also illustrated that the modified approach still had the best performance.

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