Big data-driven correlation analysis based on clustering for energy-intensive manufacturing industries

Ma, Shuaiyin; Huang, Yuming; Liu, Yang; Liu, Haizhou; Chen, Yanping; Wang, Jin; Zhang, Pengfei

doi:10.1016/j.apenergy.2023.121608

Cited by 23 publications

(3 citation statements)

References 57 publications

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“…Good career planning is also based on a correct understanding of oneself, giving full play to personal strengths, rationally planning the employment direction, and at the same time looking for the lack of employment competitiveness in combination with one's career goals and making targeted improvements [22]. Career planning is to link personal human capital with social needs.…”

Section: Know Yourself Correctly and Improve The Level Of Employment ...mentioning

confidence: 99%

Creation and Investigation of a Big Data Technology-Driven Auxiliary Employment Platform for Public Utilities Management

Lan

2023

Int. J. Appl. Inf. Manag.

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With the development of society and the advancement of science and technology, the current employment problem has gradually become hotter. At the same time, the number of college graduates has shown rapid growth. However, due to the development of science and technology in our country, the demand for labor has declined, and various factors have caused college students to find themselves in a difficult situation.The purpose of this article is to conduct research on the design of a public utility management assisted employment platform based on big data technology. Based on the analysis of the employment history and current situation of college students in New China, this paper analyzes the employment dilemma of college students from the perspective of "New Public Management", applies the concept of new public management to the public service platform of college student employment, and proposes that college students should be further improved. The degree of marketization of employment, the construction of a diversified employment service platform for college students, etc.Further clarify the focus of solving the employment problem of college students. Due to the theoretical and practical deficiencies of the new public management itself, the employment platform for college students derived from the perspective of the new public management has deficiencies. The experimental research results show that the effectiveness of the auxiliary employment platform for public utilities management is better is the auxiliary employment platform C for public utilities management, with an effectiveness of 75.83%. In general, the effective average of the auxiliary employment platform for public utilities management is 70. It can be seen that the actual use of these auxiliary employment platforms is not large, and at the same time, it also shows that there is a lot of room for.

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Section: Know Yourself Correctly and Improve The Level Of Employment ...mentioning

confidence: 99%

Creation and Investigation of a Big Data Technology-Driven Auxiliary Employment Platform for Public Utilities Management

Lan

2023

Int. J. Appl. Inf. Manag.

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“…Data collection is a precondition for analysis. The wide application of IoT in industrial production provides convenience for data acquisition [26].…”

Section: Data Analysis and Processing Stagementioning

confidence: 99%

Unlocking Insights: A Cloud Tool for Data Visualisation in a Smart Meter Project

Luyo,

Pacheco,

Cardenas

et al. 2023

Processes

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Nowadays, the large amount of data generated by society has led to a dependency on data analysis and visualisation tools. Therefore, the objective of this research was to implement a cloud-based tool to improve the visualisation of data obtained from 4G network simulation on smart meters. Two stages were carried out in order to analyse and process the data using a cloud-based tool to support data visualisation and to understand and facilitate effective decision-making. This resulted in a remarkable 27.39% increase in average data quality, thanks to the authenticity and reliability of the data obtained through the 4G LTE network on smart meters. It also had a significant impact on the percentage of data read and displayed, with an increase of 63.70%. Finally, the percentage of useful data when applying the tool in the cloud also increased by 47.30%. This allows end users to visualise and understand the behaviour of electricity meters with an LTE network through a customised dashboard.

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“…Predictive technology is the fundamental component of the PdM plan and can be broadly categorized into three distinct approaches [ 18 ]. Several methods can be employed to assess errors from a statistical standpoint such as cluster analysis [ 19 ], statistical process control (SPC) [ 20 ], hidden Markov models (HMM) [ 21 ], and other statistical methods. Another method involves utilizing model-based approaches to develop a precise and well-defined mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Estimation of MoSi2 Heating Element in a Pusher Kiln Process

Irfan,

Liao,

Taipabu

et al. 2024

Sensors

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The critical challenge of estimating the Remaining Useful Life (RUL) of MoSi2 heating elements utilized in pusher kiln processes is to enhance operational efficiency and minimize downtime in industrial applications. MoSi2 heating elements are integral components in high-temperature environments, playing a pivotal role in achieving optimal thermal performance. However, prolonged exposure to extreme conditions leads to degradation, necessitating precise RUL predictions for proactive maintenance strategies. Since insufficient failure experience deals with Predictive Maintenance (PdM) in real-life scenarios, a Generative Adversarial Network (GAN) generates specific training data as failure experiences. The Remaining Useful Life (RUL) is the duration of the equipment’s operation before repair or replacement, often measured in days, miles, or cycles. Machine learning models are trained using historical data encompassing various operational scenarios and degradation patterns. The RUL prediction model is determined through training, hyperparameter tuning, and comparisons based on the machine-learning model, such as Long Short-Term Memory (LSTM) or Support Vector Regression (SVR). As a result, SVR reflects the actual resistance variation, achieving the R-Square (R2) of 0.634, better than LSTM. From a safety perspective, SVR offers high prediction accuracy and sufficient time to schedule maintenance plans.

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Big data-driven correlation analysis based on clustering for energy-intensive manufacturing industries

Cited by 23 publications

References 57 publications

Creation and Investigation of a Big Data Technology-Driven Auxiliary Employment Platform for Public Utilities Management

Creation and Investigation of a Big Data Technology-Driven Auxiliary Employment Platform for Public Utilities Management

Unlocking Insights: A Cloud Tool for Data Visualisation in a Smart Meter Project

Remaining Useful Life Estimation of MoSi2 Heating Element in a Pusher Kiln Process

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