Improving Maintenance Strategies from Experience Feedback

Ruiz, Paula Potes; Foguem, Bernard Kamsu; Grabot, Bernard

doi:10.3182/20130619-3-ru-3018.00036

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…• The rules base: consists of some domain encoding of expertise for the system. This can be in the form of semantic nets [16], procedural representations [17], or production rules [18]. In general case, it is represented by production rules, called also "induction rules".…”

Section: The Cognitive Agent Architecturementioning

confidence: 99%

Improving the Cognitive Agent Intelligence by Deep Knowledge Classification

Chemchem

Alin

Krajecki

2019

Int. J. Comp. Intel. Appl.

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In this paper, a new idea is developed for improving the agent intelligence. In fact with the presented convolutional neural network (CNN) approach for knowledge classification, the agent will be able to manage its knowledge. This new concept allows the agent to select only the actionable rule class, instead of trying to infer its whole rule base exhaustively. In addition, through this research, we developed a comparative study between the proposed CNN approach and the classical classification approaches. As foreseeable the deep learning method outperforms the others in term of classification accuracy.

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Section: The Cognitive Agent Architecturementioning

confidence: 99%

Improving the Cognitive Agent Intelligence by Deep Knowledge Classification

Chemchem

Alin

Krajecki

2019

Int. J. Comp. Intel. Appl.

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Inequality Indices Based on the Notion of Shannon-Entropy for the Assessments of Industrial Fleets

González-Prida

Orchard

Martín

et al. 2016

Proceedings of the 10th World Congress on Engineering Asset Management (WCEAM 2015)

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Proposing a More Efficient Maintenance Scheduling for an Overhaul Maintenance Project in Engineering Asset Management

Cahyo

Hasibuan

Hendradewa

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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In maintenance activity, scheduling planning is an important step to improve the performance of maintenance department. In some cases, the data to support the process to develop the maintenance schedule is unavailable. In this research, it is argued that Genetic Algorithm (GA) is an appropriate tool to support the decision maker in order to develop the schedule. The data required in this research is obtained from the maintenance data in the sugar milling company in Yogyakarta. The company run the production of sugar and spirits/alcohol. The Production process (milling seasin) is usually performed within the harvesting months of the raw material: the sugar cane. It is approximately in the month of February to August. On the other hand, the overhaul maintenance process is performed after the milling season. The overhaul maintenance planning is created by the manager of the maintenance department, and usually is done using the manager qualitative approach without any sciencetific approach. To get a more efficient schedule of maintenance, several parameters are set in this research. The parameters are then applied as the paramenters in GA. Those are: The number of manpower required and the duration of operation each activity. To run the GA model, Python programming is used. Before start the GA process, the parent chromosom and its fitness are determined. The date of maintenance activity start per each activity is set as the parent chromosome and the fitness is determined using a formula. The process of finding the better maintenance schedule is then performed based on the procedures in GA. It is indicated from the result that the proposed schedule generated in GA required less number of manpower.

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Improving Maintenance Strategies from Experience Feedback

Cited by 3 publications

References 28 publications

Improving the Cognitive Agent Intelligence by Deep Knowledge Classification

Improving the Cognitive Agent Intelligence by Deep Knowledge Classification

Inequality Indices Based on the Notion of Shannon-Entropy for the Assessments of Industrial Fleets

Proposing a More Efficient Maintenance Scheduling for an Overhaul Maintenance Project in Engineering Asset Management

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