scholarly journals Smart Operation and Maintenance Platform of Protection Relay Based on Mobile Sensing and Big Data

2021 ◽  
Vol 632 ◽  
pp. 042021
Author(s):  
Wenwu Liang ◽  
Fan Ouyang ◽  
Shannuo Wang ◽  
Weijun Zhu ◽  
Gan Li
Keyword(s):  
Big Data ◽  
Mobile Sensing ◽  
Protection Relay ◽  
Operation And Maintenance

scholarly journals Emergent Technologies in Big Data Sensing: A Survey

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Ting Zhu ◽  
Sheng Xiao ◽  
Qingquan Zhang ◽  
Yu Gu ◽  
Ping Yi ◽  
...  
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Data Science ◽  
Mobile Sensing ◽  
Emergent Technologies ◽  
Sensing Applications ◽  
Crowd Sensing ◽  
Multiple Data ◽  
Challenges And Opportunities ◽  
Research Architecture

When the number of data generating sensors increases and the amount of sensing data grows to a scale that traditional methods cannot handle, big data methods are needed for sensing applications. However, big data is a fuzzy data science concept and there is no existing research architecture for it nor a generic application structure in the field of sensing. In this survey, we explore many scattered results that have been achieved by combining big data techniques with sensing and present our vision of big data in sensing. Firstly, we outline the application categories to generally summarize existing research achievements. Then we discuss the techniques proposed in these studies to demonstrate challenges and opportunities in this field. Finally, we present research trends and list some directions of big data in future sensing. Overall, mobile sensing and its related studies are hot topics, but other large-scale sensing researches are flourishing too. Although there are no “big data” techniques acting as research platforms or infrastructures to support various applications, multiple data science technologies, such as data mining, crowd sensing, and cloud computing, serve as foundations and bases of big data in the world of sensing.

scholarly journals Monitoring the Wind Turbine Condition Using Big Data Technique

2019 ◽  
Vol 8 (S1) ◽  
pp. 98-102
Author(s):  
N. V. Poorima ◽  
B. Srinivasan ◽  
S. Karthikeyan
Keyword(s):  
Big Data ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farms ◽  
Observation System ◽  
Operation And Maintenance ◽  
Machine Deterioration

The desire to cut back the price of energy from turbine generation has seen a rise within the analysis applied to the sphere of turbine condition observation. Wind turbine condition observation has the potential to cut back operation and maintenance prices through optimized maintenance programming and also the rejection of major breakdowns. To aid this analysis, increasing volumes of knowledge are being captured and keep. These massive volumes of knowledge could also be deemed ‘Big Data’, and need improved handling techniques so as to figure with the information with efficiency. It introduces a turbine condition observation system that has been put in in AN operational Vestas V47 turbine for the aim of developing algorithms to sight machine deterioration. The system’s ability to capture massive volumes of knowledge (approx.2TB per month) has LED to the need of victimization increased knowledge handling techniques. This paper can discuss these ‘Big Data’ techniques and recommend however they will ultimately be used for condition observation of multiple wind turbines or wind farms.

scholarly journals An Advanced Operation Mode with Product-Service System Using Lifecycle Big Data and Deep Learning

2021 ◽  
Author(s):  
Shan Ren ◽  
Yingfeng Zhang ◽  
Tomohiko Sakao ◽  
Yang Liu ◽  
Ruilong Cai
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Environmental Sustainability ◽  
Service System ◽  
Operation Mode ◽  
Fault Prediction ◽  
Cnc Machine ◽  
Product Service System ◽  
Operation And Maintenance ◽  
Product Service

AbstractAs a successful business strategy for enhancing environmental sustainability and decreasing the natural resource consumption of societies, the product-service system (PSS) has raised significant interests in the academic and industrial community. However, with the digitisation of the industry and the advancement of multisensory technologies, the PSS providers face many challenges. One major challenge is how the PSS providers can fully capture and efficiently analyse the operation and maintenance big data of different products and different customers in different conditions to obtain insights to improve their production processes, products and services. To address this challenge, a new operation mode and procedural approach are proposed for operation and maintenance of bigger cluster products, when these products are provided as a part of PSS and under exclusive control by the providers. The proposed mode and approach are driven by lifecycle big data of large cluster products and employs deep learning to train the neural networks to identify the fault features, thereby monitoring the products’ health status. This new mode is applied to a real case of a leading CNC machine provider to illustrate its feasibility. Higher accuracy and shortened time for fault prediction are realised, resulting in the provider’s saving of the maintenance and operation cost.

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