State of product detection method applicable to Industry 4.0 manufacturing models with small quantities and great variety: An example with springs

Kuo, Chengi; Ting, Kuo-Cheng; Chen, Yi-Chung

doi:10.1109/icasi.2017.7988251

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…The main objective of this algorithm is to project the data into a lower-dimensional space that maximizes the between-class variance while minimizing the within-class variance [42]. Some of the applications of LDA on Industry 4.0 include state of product detection [43], malfunction monitoring systems [44], EEG hand movement classification [45], etc. That being said, let us explore the theory behind LDA.…”

Section: Linear Discriminant Analysismentioning

confidence: 99%

Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems

et al. 2021

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One of the main focuses of Education 4.0 is to provide students with knowledge on disruptive technologies, such as Machine Learning (ML), as well as the skills to implement this knowledge to solve real-life problems. Therefore, both students and professors require teaching and learning tools that facilitate the introduction to such topics. Consequently, this study looks forward to contributing to the development of those tools by introducing the basic theory behind three machine learning classifying algorithms: K-Nearest-Neighbor (KNN), Linear Discriminant Analysis (LDA), and Simple Perceptron; as well as discussing the diverse advantages and disadvantages of each method. Moreover, it is proposed to analyze how these methods work on different conditions through their implementation over a test bench. Thus, in addition to the description of each algorithm, we discuss their application to solving three different binary classification problems using three different datasets, as well as comparing their performances in these specific case studies. The findings of this study can be used by teachers to provide students the basic knowledge of KNN, LDA, and perceptron algorithms, and, at the same time, it can be used as a guide to learn how to apply them to solve real-life problems that are not limited to the presented datasets.

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Section: Linear Discriminant Analysismentioning

confidence: 99%

Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems

et al. 2021

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“…In consideration of this, many researchers have been developing inexpensive ways to help these manufacturers achieve Industry 4.0. For instance, Kuo et al [4] [9] proposed methods that combine machine maintenance personnel experience, add-on triaxial accelerometers, and artificial neural networks for machinery fault detection and successfully applied these methods to spring factories. Kuo et al [5]successfully developed an RFID-based component management system that records what components a machine contains and what processing task it is performing, thereby greatly increasing component management efficiency.…”

Section: Introductionmentioning

confidence: 99%

Multi-tasking multi-machine scheduling system for multi-stage multi-criteria production

et al. 2018

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In recent years, many major manufacturers have been incorporating Industry 4.0 technologies such as preventive fault detection, automated scheduling algorithms, and component management to increase productivity and reduce production costs. Achieving this objective requires a substantial amount of working capital to acquire large quantities of new machinery, equipment to extract data from the machinery, and high-priced big data analysis software. However, most factories in the world are small-or medium-sized companies and have not enough capital to replace their machinery or purchase big data analysis software. It is therefore almost impossible for these factories to reach the goal of Industry 4.0. Furthermore, most of the conventional automated production scheduling methods only consider a single criterion in scheduling, which is not applicable for actual situations. This study therefore proposed a multi-tasking multi-machine scheduling system for multi-stage multi-criteria production to address various shortcomings in existing methods. To achieve this goal, we proposed a novel concept based on skyline queries to assist in the scheduling process. Also, a data structure of "heap" is applied in this work to accelerate the scheduling process. The experimental results demonstrated the validity of the proposed approach.

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Anomaly detection in discrete manufacturing using self-learning approaches

et al. 2019

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State of product detection method applicable to Industry 4.0 manufacturing models with small quantities and great variety: An example with springs

Cited by 5 publications

References 6 publications

Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems

Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems

Multi-tasking multi-machine scheduling system for multi-stage multi-criteria production

Anomaly detection in discrete manufacturing using self-learning approaches

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