A Study on Prediction Model of Equipment Failure Through Analysis of Big Data Based on RHadoop

Ku, Jin-Hee

doi:10.1007/s11277-017-4151-1

Cited by 23 publications

(8 citation statements)

References 9 publications

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“…Through this statistic, it can be shown that the researches of big-data based solutions focus on monitoring, prediction, data analytics and propose ICT solutions in manufacturing in Table 3. [57], [56], [125], Regressions [53], ANN [53], PLM [58], Production ERP MP [63], regression [62], K-means [126] MOM/MES [69], [116], [66], regression [127], Distance, Regression, Selforganizing map, principal component analysis [128], SCADA/DCS/ HMI [129], [71], [104], [130], [131], [104], [132] , [112] Classification [133], OPL [106], KM [134], GA [73], O&M [39], [135] [136], [81], [82], [80] logistic regression, naïve Bayes, and a decision tree [109], regression [137], LSM [138], SVM [83], Anomaly detection [139], DTW [140], RF [141], K-means, Markov [142], KD [24],…”

Section: Applications Of Big Data In Manufacturingmentioning

confidence: 99%

“…Streaming (Flink, Storm), micro-batching (Spark) and batching (MapReduce) data processing technologies provide the capabilities to clean and calculate big volume of manufacturing data. The following big-data based solutions are proposed and implemented in various manufacturing applications: complex event processing (CEP) with Storm [138], anomaly detection with Flink [121], industrial process control with Spark [73], model prediction [137] and quality control with MapReduce [88].…”

Section: Driver 2: Datamentioning

confidence: 99%

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Manufacturing big data ecosystem: A systematic literature review

Cui

Kara

Chan

2020

Robotics and Computer-Integrated Manufacturing

234

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Advanced manufacturing is one of the core national strategies in the US (AMP), Germany (Industry 4.0) and China (Made-in China 2025). The emergence of the concept of Cyber Physical System (CPS) and big data imperatively enable manufacturing to become smarter and more competitive among nations. Many researchers have proposed new solutions with big data enabling tools for manufacturing applications in three directions: product, production and business.Big data has been a fast-changing research area with many new opportunities for applications in manufacturing. This paper presents a systematic literature review of the state-of-the-art of big data in manufacturing. Six key drivers of big data applications in manufacturing have been identified. The key drivers are system integration, data, prediction, sustainability, resource sharing and hardware.Based on the requirements of manufacturing, nine essential components of big data ecosystem are captured. They are data ingestion, storage, computing, analytics, visualization, management, workflow, infrastructure and security.Several research domains are identified that are driven by available capabilities of big data ecosystem. Five future directions of big data applications in manufacturing are presented from modelling and simulation to realtime big data analytics and cybersecurity.

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Section: Applications Of Big Data In Manufacturingmentioning

confidence: 99%

Section: Driver 2: Datamentioning

confidence: 99%

Manufacturing big data ecosystem: A systematic literature review

Cui

Kara

Chan

2020

Robotics and Computer-Integrated Manufacturing

234

View full text Add to dashboard Cite

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“…Health is the basic right of human beings, and health is the first wealth of life [1]. To keep healthy and keep away from disease is to have happiness.…”

Section: Introductionmentioning

confidence: 99%

Analysis Method of Motion Information Driven by Medical Big Data

2019

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Nowadays, with the maturity and development of Internet, cloud computing and Internet of things technology, and the wide popularization of medical / health information technology, medical / health related data is growing at an amazing speed. At the same time, the popularization and application of genomic technology and the rapid development of wearable mobile medical and mobile health technology, promote the field of health care to enter the era of big data. Traditional sports risk assessment methods can assess the risks in sports, but there are problems such as time-consuming and poor accuracy in the assessment process, which can not be used in large-scale sports assessment. A method of motion risk assessment based on big data analysis is proposed. Based on the analysis of risk factors, this paper constructs a risk assessment model of large-scale sports, and introduces multi-level superposition operation and multi-factor mediation variance method, processes the sports risk data, and realizes the sports risk assessment using BP neural network based on the big data analysis method. The experimental results show that the proposed method based on big data analysis, compared with the traditional risk assessment method, can carry out high-efficiency and accurate motion risk assessment, and can be applied to large-scale risk assessment. This system has theoretical and practical significance for guiding the theory and practice of big data driven health / disease management, promoting the transformation and upgrading of sports health management and the development of health care big data industrialization.INDEX TERMS Data analysis, big data, cloud platform, motion information, EEMD-HHT.

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“…Cloud computing and Internet of Things (IoT) are two hot research directions in IT field and scientific research institutes in recent two years. Cloud computing is an industry model and technology system that integrates and abstracts IT resources to users [1], [2]. It is a distributed basic platform that integrates computing capacity, storage capacity, broadband capacity and service capacity.…”

Section: Introductionmentioning

confidence: 99%

Energy Analysis and Application of Data Mining Algorithms for Internet of Things Based on Hadoop Cloud Platform

Zheng

Chen

2019

IEEE Access

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The paper analyses and studies the classification and characteristics of Internet of Things (IoT) information, and discusses the construction and application of Hadoop Cloud Platform. This paper mainly carries out from two aspects. One is to design the system architecture of the Open Platform for Data Simulation Resources of the IoT and design the key modules. A platform for data simulation resources of the IoT is built to provide the running environment and external services for the sensor data simulation model established. On the other hand, it is the key method to study the simulation data model based on IoT sensors. That is, based on the research environment of the IoT built by the existing laboratories, collect the data of sensors, analyze and study the characteristics of sensors in the IoT, and design the key algorithms for data simulation. This paper presents two key models for sensor data modeling: the Long Short-term Memory (LSTM) prediction model and the Support Vector Machine (SVM) model based on IoT data, which are suitable for different data volumes. Extensive simulations are executed to validate the remarkable nature in Hadoop platform, in terms of prediction accuracy and training efficiency under different working condition. INDEX TERMS Energy analysis, Internet of Things, cloud platform, Hadoop.

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A Study on Prediction Model of Equipment Failure Through Analysis of Big Data Based on RHadoop

Cited by 23 publications

References 9 publications

Manufacturing big data ecosystem: A systematic literature review

Manufacturing big data ecosystem: A systematic literature review

Analysis Method of Motion Information Driven by Medical Big Data

Energy Analysis and Application of Data Mining Algorithms for Internet of Things Based on Hadoop Cloud Platform

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