Immune inspired Fault Detection and Diagnosis: A fuzzy-based approach of the negative selection algorithm and participatory clustering

Silva, Guilherme Costa; Palhares, Reinaldo M.; Caminhas, Walmir M.

doi:10.1016/j.eswa.2012.04.066

Cited by 36 publications

(4 citation statements)

References 11 publications

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“…On the other hand, the design of a second bank of calculated models, including the variation in the set point, could be considered: A variation of 10% of the original reference value is taken as an example, and the models are calculated in the same way as the previous case [ 22 , 44 , 45 , 46 ]. A new alternative for fault detection and diagnosis was proposed based on and taking advantage of the characteristics of a method originally used for the control of processes with different operating regimes [ 23 , 27 , 47 , 48 , 49 ]. One of the advantages of the proposed method is the use of only the input signal (control signal) and the output of the process to obtain the models.…”

Section: Discussion and Analysis Of The Existing Literaturementioning

confidence: 99%

“…As a higher number of failures are taken into consideration, the number of models will expand. This may be an annoyance if new failures occur; however, it is also feasible to regroup the models according to the characteristics that they exhibit [ 40 , 47 , 54 , 55 ]. In addition to the benefits that have already been described, this strategy also has the advantage of being able to take into consideration a wider range of values for potential failures, even when using offline models [ 2 , 11 , 42 , 56 ].…”

Section: Discussion and Analysis Of The Existing Literaturementioning

confidence: 99%

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Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis

Mercorelli

2024

Sensors

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Fault-finding diagnostics is a model-driven approach that identifies a system’s malfunctioning portion. It uses residual generators to identify faults, and various methods like isolation techniques and structural analysis are used. However, diagnostic equipment doesn’t measure the remaining signal-to-noise ratio. Residual selection identifies fault-detecting generators. Fault detective diagnostic (FDD) approaches have been investigated and implemented for various industrial processes. However, industrial operations make it difficult to implement FDD techniques. To bridge the gap between theoretical methodologies and implementations, hybrid approaches and intelligent procedures are needed. Future research should focus on improving fault prognosis, allowing for accurate prediction of process failures and avoiding safety hazards. Real-time and comprehensive FDD strategies should be implemented in the age of big data.

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Section: Discussion and Analysis Of The Existing Literaturementioning

confidence: 99%

Section: Discussion and Analysis Of The Existing Literaturementioning

confidence: 99%

Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis

Mercorelli

2024

Sensors

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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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“…There have been an extensive number of studies on NSAs in the literature resulting various algorithm modifications and applications [2]. Since its introduction, NSAs have been applied in computer virus detection [3][4], intrusion detection [5], anomaly detection [6][7][8], monitoring UNIX processes, scheduling [9], fault detection and diagnosis [10], email spam detection [11], to name but a few.…”

Section: Introductionmentioning

confidence: 99%

A Novel Negative Selection Algorithm with Optimal Worst-case Training Time Complexity for R-chunk Detectors

Trưởng¹

2020

IJST

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Objectives: To generate complete and non-redundant detector set with optimal worst-case time complexity. Methods: In this study, a novel exact matching and string-based Negative Selection Algorithm utilizing r-chunk detectors is proposed. Improved algorithms are tested on some data sets; the experiments' results are compared with recently published ones. Moreover, algorithms' complexities are also proved mathematically. Findings: For string-based Artificial Immune Systems, r-chunk detector is the most common detector type and their generation complexity is one of the important factors considered in the literature. We proposed optimal algorithms based on automata to present all detectors. Novelty/applications: The algorithm could generate the representation of complete and nonredundant detector set with optimal worst-case time complexity. To the best of our knowledge, the algorithm is the first one to possess such worst-case training time complexity.

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Immune inspired Fault Detection and Diagnosis: A fuzzy-based approach of the negative selection algorithm and participatory clustering

Cited by 36 publications

References 11 publications

Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis

Recent Advances in Intelligent Algorithms for Fault Detection and Diagnosis

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

A Novel Negative Selection Algorithm with Optimal Worst-case Training Time Complexity for R-chunk Detectors

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