Development of an intelligent decision making tool for maintenance planning using fuzzy logic and dynamic scheduling

Andrew, Alan W.; Kumanan, S.

doi:10.1007/s41870-019-00384-w

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…e implementation process of data mining involves machine learning, pattern recognition, statistical analysis, distributed storage, distributed computing, visualization, etc., in which knowledge of specialized fields is also required. In other words, data mining is a process of extracting potential, comprehensible, and valuable information from a large amount of fuzzy, incomplete, and random data of various types using various methods [12]. Currently, data mining techniques are gaining attention and importance from both academic and business communities and are widely used in various fields.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

2021

Complexity

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As the number of students in universities continues to grow, the university academic management system has a large amount of data on student performance. However, the utilization of these data is only limited to simple query and statistical work, and there is no precedent of using these data for improving English teaching mode. With the application of fuzzy theory in machine learning and artificial intelligence, the fuzzy decision tree algorithm was born by integrating fuzzy set theory with decision tree algorithm. In this paper, we propose a way to obtain the centroids of continuous attribute clustering by K-means algorithm and combine the triangular fuzzy number to fuzzy the continuous data. In addition, this paper analyzes the influence of nearest neighbor distance on classification, introduces Gaussian weight function, gives different voting weights to the neighborhood according to the distance, and establishes a weighted K-nearest neighbor classification algorithm. To address the problem of low classification efficiency of K-nearest neighbor algorithm when the dataset is large, this paper further improves the algorithm and establishes the partitioned weighted K-nearest neighbor algorithm. The classification time was shortened from 11.39 seconds to 5.22 seconds, and the classification efficiency greatly improved.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

2021

Complexity

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“…To address this problem, fuzzy logic is a powerful tool for modeling and controlling this type of equipment [ 27 ]. Referring to the literature [ 28 , 29 , 30 , 31 , 32 , 33 ], fuzzy logic has been used in maintenance as a decision-making, scheduling, and hazard-level reckoning tool.…”

Section: Data Gathering and Processingmentioning

confidence: 99%

On-Device IoT-Based Predictive Maintenance Analytics Model: Comparing TinyLSTM and TinyModel from Edge Impulse

Mihigo

Zennaro

Uwitonze

et al. 2022

Sensors

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A precise prediction of the health status of industrial equipment is of significant importance to determine its reliability and lifespan. This prediction provides users information that is useful in determining when to service, repair, or replace the unhealthy equipment’s components. In the last decades, many works have been conducted on data-driven prognostic models to estimate the asset’s remaining useful life. These models require updates on the novel happenings from regular diagnostics, otherwise, failure may happen before the estimated time due to different facts that may oblige rapid maintenance actions, including unexpected replacement. Adding to offline prognostic models, the continuous monitoring and prediction of remaining useful life can prevent failures, increase the useful lifespan through on-time maintenance actions, and reduce the unnecessary preventive maintenance and associated costs. This paper presents the ability of the two real-time tiny predictive analytics models: tiny long short-term memory (TinyLSTM) and sequential dense neural network (DNN). The model (TinyModel) from Edge Impulse is used to predict the remaining useful life of the equipment by considering the status of its different components. The equipment degradation insights were assessed through the real-time data gathered from operating equipment. To label our dataset, fuzzy logic based on the maintainer’s expertise is used to generate maintenance priorities, which are later used to compute the actual remaining useful life. The predictive analytic models were developed and performed well, with an evaluation loss of 0.01 and 0.11, respectively, for the LSTM and model from Edge Impulse. Both models were converted into TinyModels for on-device deployment. Unseen data were used to simulate the deployment of both TinyModels. Conferring to the evaluation and deployment results, both TinyLSTM and TinyModel from Edge Impulse are powerful in real-time predictive maintenance, but the model from Edge Impulse is much easier in terms of development, conversion to Tiny version, and deployment.

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“…The study inferred that the applied approach imparts satisfactory results that are affiliated with the present Modified Early Warning Score system. Andrew et al (2020), in their work, emphasized the utilization of fuzzy logic in decision-making for sustenance planning of the manufacturing equipment by using main inputs. The average time between failures, the average time to repair, availability of spares and the age of the material, which will ultimately help the maintenance engineers and plant engineers to plan their activities productively, were evaluated for their potential to be considered as primary inputs.…”

Section: Fuzzy Logicmentioning

confidence: 99%

Water and Energy Management in India

2021

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translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Development of an intelligent decision making tool for maintenance planning using fuzzy logic and dynamic scheduling

Cited by 7 publications

References 27 publications

[Retracted] Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

[Retracted] Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement

On-Device IoT-Based Predictive Maintenance Analytics Model: Comparing TinyLSTM and TinyModel from Edge Impulse

Water and Energy Management in India

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