Fault detection, diagnosis and recovery using Artificial Immune Systems: A review

Bayar, Nawel; Darmoul, Saber; Hajri-Gabouj, Sonia; Pierreval, Henri

doi:10.1016/j.engappai.2015.08.006

Cited by 95 publications

(40 citation statements)

References 48 publications

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“…Each node in the summation layer performs an average summation operation with each group or class output of pattern layer. The output of the summation layer calculates the average probability density function of the pattern layer computations using Equation (9).…”

Section: Fault Classification Phasementioning

confidence: 99%

“…To diagnose the faulty sensor nodes, there are various types of fault diagnosis algorithms available in the literature, which are based on different approaches such as statistics, 5 neighboring coordination, 6 comparison based, 7 and neural network. 8,9 The primary role of the immune system is to protect the body from the foreign pathogens, called antigens such as bacteria and viruses. The way the clonal selection principle of AIS resembles to that of human immune system, it is possible to draw a resemblance of the clonal selection principle with the fault diagnosis in WSN.…”

Section: Introductionmentioning

confidence: 99%

“…The immune system has drawn significant attention to solve the complex computational problems. 8,9 The primary role of the immune system is to protect the body from the foreign pathogens, called antigens such as bacteria and viruses. It is used to distinguish between the body's own cells (self) and the foreign cells (non-self or antigen).…”

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

Mohapatra

Khilar

Swain

2019

Int J Communication

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The fault diagnosis in wireless sensor networks is one of the most important topics in the recent years of research work. The problem of fault diagnosis in wireless sensor network can be resembled with artificial immune system in many different ways. In this paper, a detection algorithm has been proposed to identify faulty sensor nodes using clonal selection principle of artificial immune system, and then the faults are classified into permanent, intermittent, and transient fault using the probabilistic neural network approach. After the actual fault status is detected, the faulty nodes are isolated in the isolation phase. The performance metrics such as detection accuracy, false alarm rate, false-positive rate, fault classification accuracy, false classification rate, diagnosis latency, and energy consumption are used to evaluate the performance of the proposed algorithm. The simulation results show that the proposed algorithm gives superior results as compared with existing algorithms in terms of the performance metrics. The fault classification performance is measured by fault classification accuracy and false classification rate. It has also seen that the proposed algorithm provides less diagnosis latency and consumes less energy than that of the existing algorithms proposed by Mohapatra et al, Panda et al, and Elhadef et al for wireless sensor network. KEYWORDS artificial immune system, clonal selection principle, fault diagnosis, probabilistic neural network, wireless sensor networks Int J Commun Syst. 2019;32:e4138.wileyonlinelibrary.com/journal/dac

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Section: Fault Classification Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

Mohapatra

Khilar

Swain

2019

Int J Communication

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“…11 These features have inspired many AISs, which are computational intelligence techniques directed toward engineering problem solving. 26 Artificial immune systems are widely used in many domains, such as traffic control, 27,28 monitoring and control, [29][30][31] fault detection and anomaly recovery, [32][33][34] optimization, [35][36][37][38] and mobile robotics. 39,40 More closely related to public transportation engineering, Masmoudi et al 41 suggested a preliminary analogy and a prototype implementation of an AIS for public transport regulation.…”

Section: Biological and Artificial Immune Systemsmentioning

confidence: 99%

An immune multiagent system to monitor and control public bus transportation systems

Mnif

Darmoul

Elkosantini

et al. 2018

Computational Intelligence

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In public bus transportation systems, several types of disturbances, such as accidents and congestion, may affect preestablished timetables and visiting hours at stations. Disturbances result in detrimental consequences, degraded performance, and poor quality of service in terms of extended delays and waiting times, punctuality, frequency, and efficiency of shuttles. Despite numerous research on the monitoring and control of public transportation systems by means of buses, distributed monitoring and control architectures that confer intelligence and decision-making autonomy to buses to react to disturbances are still missing. This article addresses this gap by designing and developing a distributed architecture to monitor and control public transportation buses using multiagent systems. The design relies on biological immunity as a methodological framework that guides the development of knowledge models and decision-making mechanisms. Knowledge models structure knowledge about disturbances and control decisions, whereas decision-making mechanisms implement control and reaction strategies. Through experimental validation based on simulation, we show that the suggested immune multiagent distributed control architecture is not only able to maintain performance (average delay/earliness, average total time in simulated network) at acceptable levels but also to improve quality of service in terms of number of served passengers and stations by at least 15% in case of disturbances. KEYWORDS artificial immune system, biological immune system, immune memory, multiagent system, negative selection theory, public transport control system

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“…Multi-unit systems have also been studied in the field of Distributed Decision Making, which deals with problems involving multiple decisions aimed to optimise an objective function [25]. However, Distributed Decision Making is only scarcely incorporated into maintenance, usually coupled to human decisions [26], and artificial immune systems [27]. A largely unexplored field of Distributed Decision Making is collaborative health management, aimed to study how assets could collaborate with each other in order to improve maintenance and operational policies.…”

Section: Introductionmentioning

confidence: 99%

Collaborative prognostics in Social Asset Networks

Palau

Liang

Lütgehetmann³

et al. 2019

Future Generation Computer Systems

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With the spread of Internet of Things (IoT) technologies, assets have acquired communication, processing and sensing capabilities. In response, the field of Asset Management has moved from fleet-wide failure models to individualised asset prognostics. Individualised models are seldom truly distributed, and often fail to capitalise the processing power of the asset fleet. This leads to hardly scalable machine learning centralised models that often must find a compromise between accuracy and computational power. In order to overcome this, we present a novel theoretical approach to collaborative prognostics within the Social Internet of Things. We introduce the concept of Social Asset Networks, defined as networks of cooperating assets with sensing, communicating and computing capabilities. In the proposed approach, the information obtained from the medium by means of sensors is synthesised into a Health Indicator, which determines the state of the asset. The Health Indicator of each asset evolves according to an equation determined by a triplet of parameters. Assets are given the form of the equation but they ignore their parametric values. To obtain these values, assets use the equation in order to perform a non-linear least squares fit of their Health Indicator data. Using these estimated parameters, they are interconnected to a subset of collaborating assets by means of a similarity metric. We show how by simply interchanging their estimates, networked assets are able to precisely determine their Health Indicator dynamics and reduce maintenance costs. This is done in real time, with no centralised library, and without the need for extensive historical data. We compare Social Asset Networks with the typical self-learning and fleet-wide approaches, and show that Social Asset Networks have a faster convergence and lower cost. This study serves as a conceptual proof for the potential of collaborative prognostics for solving maintenance problems, and can be used to justify the implementation of such a system in a real industrial fleet.

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Fault detection, diagnosis and recovery using Artificial Immune Systems: A review

Cited by 95 publications

References 48 publications

Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

Fault diagnosis in wireless sensor network using clonal selection principle and probabilistic neural network approach

An immune multiagent system to monitor and control public bus transportation systems

Collaborative prognostics in Social Asset Networks

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