Recent advances and trends in predictive manufacturing systems in big data environment

Lee, Jay; Lapira, Edzel; Bagheri, Behrad; Kao, Hung-An

doi:10.1016/j.mfglet.2013.09.005

Cited by 873 publications

(429 citation statements)

References 2 publications

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“…A CPS has two components, physical and cyber, that are interconnected. The cyber system deploys a "digital twin" of the real system, which can be considered as a mirrored image of the real machines and operations [103]. While the real system operates in the physical world, the digital twin operates in the cloud platform, simulates the health condition of each individual machine in the system, and continuously records and tracks machine conditions, energy consumption, product quality, and all kinds of information.…”

Section: Concurrent Design Of Product Manufacturing Systems and Busmentioning

confidence: 99%

Reconfigurable manufacturing systems: Principles, design, and future trends

2017

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Reconfigurable manufacturing systems (RMSs), which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are costeffective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.

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Section: Concurrent Design Of Product Manufacturing Systems and Busmentioning

confidence: 99%

Reconfigurable manufacturing systems: Principles, design, and future trends

2017

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“…Big data may push the next revolution in manufacturing-forecast manufacturing. In order to become more competitive, manufacturers need to accept emerging technologies, such as advanced analysis and physical network [12], to improve their efficiency and productivity based on a systematic approach. Big data can help reduce defects and control costs during automated production.…”

Section: Manufacturingmentioning

confidence: 99%

Research of Big Data Based on the Views of Technology and Application

Mo¹,

Li²

2015

AJIBM

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In the era of big data, large amounts of data affect our work, life and study, even national economic development. It provides a new way of thinking and approaches to analyze and solve problems, which gradually becomes a hot research. Based on describing the concept and characteristics of big data, this paper describes the development of technologies in big data analysis and storage and analyses the trends and different values in commercial applications, manufacturing, biomedical science and other applications. At last, the authors sum up the existent challenges of big data applications and put forward the view that we should deal with big data challenges correctly.

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“…Imbalanced problem consider two classes of one is represented by large samples and another one is represented by lowest samples from the training data and output data sets fit the best data [7].Data set context in the big data can be divided into structured and unstructured data have an attribute such as feature extraction and feature reduction [8]. This will handle sampling, transformation, denoising, and normalization under this selection of representative subset, earn single input, removal of noise and feature extraction can be done.…”

Section: Introductionmentioning

confidence: 99%

Efficient DANNLO Classifier for Multi-class Imbalanced Data on Hadoop

2017

IJMTER

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Abstract--In recent years, multi-class imbalance data classification is a major problem in big data. In such situations, we focused on developing a new Deep Artificial Neural Network Learning Optimization (DANNLO) Classifier for large collection of imbalanced data.In our proposed work, first the dataset reduction using principal component analysis for dimensionality reduction and initial centroid is computed.Then, parallel hierarchical Pillar k-means clustering algorithm based on MapReduceis usedto partitioning of an imbalanced data set into similar subset, which can improve the computational cost. The resultant clusters are given as input to the deep ANN for learning. In the next stage, deep neural network has been trained using the back propagation algorithm. In order to optimize the n-dimensional weight space, firefly optimization algorithm is used. Attractiveness and distance of each firefly is computed. Hadoop is used to handle these large volumes of variable size data. Imbalanced datasets is taken from ECDC (European Centre for Disease Prevention and Control) repository.The experimental results illustrated that the proposed method can significantly improve the effectiveness in classifying imbalanced data based on TP rate, F-measure, G-mean measures, confusion matrix, precision, recall, and ROC. The experimental results suggests that DANNLO classifier exceed other ordinary classifiers such as SVM and Random forest classifier on tested imbalanced data sets.

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Recent advances and trends in predictive manufacturing systems in big data environment

Cited by 873 publications

References 2 publications

Reconfigurable manufacturing systems: Principles, design, and future trends

Reconfigurable manufacturing systems: Principles, design, and future trends

Research of Big Data Based on the Views of Technology and Application

Efficient DANNLO Classifier for Multi-class Imbalanced Data on Hadoop

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