Proceedings ArticleDOI
Cloud-based machine learning for predictive analytics: Tool wear prediction in milling
Dazhong Wu,Connor Jennings,Janis P. Terpenny,Soundar R. T. Kumara +3 more
- pp 2062-2069
TLDR
This research creates a novel approach for machinery prognostics using a cloud-based parallel machine learning algorithm applied to predict tool wear in dry milling operations using the MapReduce framework and the Amazon Elastic Compute Cloud.Abstract:
The proliferation of real-time monitoring systems and the advent of Industrial Internet of Things (IIoT) over the past few years necessitates the development of scalable and parallel algorithms that help predict mechanical failures and remaining useful life of a manufacturing system or system components. Classical model-based prognostics require an in-depth physical understanding of the system of interest and oftentimes assume certain stochastic or random processes. To overcome the limitations of model-based methods, data-driven methods such as machine learning have been increasingly applied to prognostics and health management (PHM). While machine learning algorithms are able to build accurate predictive models, large volumes of training data are required. Consequently, machine learning techniques are not computationally efficient for data-driven PHM. The objective of this research is to create a novel approach for machinery prognostics using a cloud-based parallel machine learning algorithm. Specifically, one of the most popular machine learning algorithms (i.e., random forest) is applied to predict tool wear in dry milling operations. In addition, a parallel random forest algorithm is developed using the MapReduce framework and then implemented on the Amazon Elastic Compute Cloud. Experimental results have shown that the random forest algorithm can generate very accurate predictions. Moreover, significant speedup can be achieved by implementing the parallel random forest algorithm.read more
Citations
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Journal ArticleDOI
Digital Twin: Enabling Technologies, Challenges and Open Research
TL;DR: Digital twins as discussed by the authors is an emerging concept that has become the centre of attention for industry and, in recent years, academia and a review of publications relating to Digital Twins is performed, producing a categorical review of recent papers.
Journal ArticleDOI
Parallel Optimization: Theory, Algorithms and Applications
TL;DR: Yair Censor and Stavros A. Zenios, Oxford University Press, New York, 1997, 539 pp.
Journal ArticleDOI
Predictive maintenance in the Industry 4.0: A systematic literature review
Tiago Zonta,Cristiano André da Costa,Rodrigo da Rosa Righi,Miromar Jose de Lima,Eduardo Silveira da Trindade,Guann-Pyng Li +5 more
TL;DR: It was concluded that computer science, including artificial intelligence and distributed computing fields, is more and more present in an area where engineering was the dominant expertise, so detaching the importance of a multidisciplinary approach to address Industry 4.0 effectively.
Journal ArticleDOI
Cloud and IoT based disease prediction and diagnosis system for healthcare using Fuzzy neural classifier
TL;DR: A new systematic approach is used for the diabetes diseases and the related medical data is generated by using the UCI Repository dataset and the medical sensors for predicting the people who has affected with diabetes severely and a new classification algorithm called Fuzzy Rule based Neural Classifier is proposed for diagnosing the disease and the severity.
Journal ArticleDOI
Data-Driven Methods for Predictive Maintenance of Industrial Equipment: A Survey
TL;DR: This paper focuses on data-driven methods for PdM, presents a comprehensive survey on its applications, and attempts to provide graduate students, companies, and institutions with the preliminary understanding of the existing works recently published.
References
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Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This paper presents the implementation of MapReduce, a programming model and an associated implementation for processing and generating large data sets that runs on a large cluster of commodity machines and is highly scalable.
Journal ArticleDOI
MapReduce: simplified data processing on large clusters
Jeffrey Dean,Sanjay Ghemawat +1 more
TL;DR: This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.
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