Power Modeling for an Industrial Installation

Kline, John; Kline, Charles H.

doi:10.1109/tia.2017.2734038

Cited by 4 publications

(4 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Complex industrial processes often have a large number of devices and components, each consuming di erent amount of energies and working in di erent states [11]. Parallel to this, to enhance industrial acceptance, it is crucial to design the monitoring system to be minimal invasive, such that sensing, data acquisition and transmission cause little interruptions to the process [12].…”

Section: Energy E Ciency In Industry 40mentioning

confidence: 99%

A remote MQTT-based data monitoring system for energy efficiency in industrial environments

Lopes

Fernandes

2021

VETOR

View full text Add to dashboard Cite

The concept of the new industry seeks not only to improve production processes, but also to bring solutions to environmental problems, in addition to reducing resource consumption, while maintaining high yields. This constant search for process optimization has been the main agent in the development of new technologies aimed at improving the performance of industrial production lines. Thus, this article proposes to raise some important concepts of Industry 4.0, and present the development of a remote IoT-based system that, through MQTT and Modbus protocols, will be responsible for monitoring the entire electrical network of an industrial plant, sending its data to the cloud, where it can be monitored and analyzed by the industry management sector or even by an artificial intelligence system, in a simple and effective way, in real time and from anywhere, in order to assist in decision-making focused on energy efficiency.

show abstract

Section: Energy E Ciency In Industry 40mentioning

confidence: 99%

A remote MQTT-based data monitoring system for energy efficiency in industrial environments

Lopes

Fernandes

2021

VETOR

View full text Add to dashboard Cite

show abstract

“…These meters are installed inside the factory's electrical panels, using hardwired connections for voltage measurements with a current transformer installed on each phase to measure current. As the large machines have independently wired supplies inside the panels, the system is able to gather a granular picture of electricty usage [26]. In addition to this, pre-existing gas and water meters produce pulse outputs which are captured via GPIO triggered interrupts.…”

Section: Point Energy Platformmentioning

confidence: 99%

Analysis of energy saving potentials in intelligent manufacturing: A case study of bakery plants

Wang

Gan

et al. 2019

Energy

View full text Add to dashboard Cite

To address the global challenge of the climate change, more strict legislations worldwide on carbon emission reductions have put energy intensive industries under immense pressure to improve the energy efficiency. Due to the lack of technical support and financial incentives, a range of technical and economic barriers still exist for small-medium enterprises (SMEs). This paper first introduces a point energy technology, which is developed for SMEs to improve the insight of the energy usage in the manufacturing processes and installed in a local bakery. Statistical analysis of electricity consumption data over a sevenday period is conducted, including the identification of operational modes for individual processing units using an enhanced clustering method and the voltage unbalance conditions associated with these identified modes. Two technical strategies, namely electrical load allotment and voltage unbalance minimisation, are then proposed, which could attain more than 800 kwh energy saving during this period and the current unbalance could be reduced to less than 10%. In

show abstract

“…The data acquisition layer is composed of a series of microcontroller nodes that form a wireless sensor network (WSN) using LoRa as the radio interconnect. These nodes are responsible for continuously measuring power, voltage, current and power factor for the three phase systems in the factory [22]. The management of the WSN is done by an on-site base station which is composed of a server, a LoRa concentrator and a 3G/4G internet connection.…”

Section: Energy Monitoring Systemmentioning

confidence: 99%

Data Augmentation for Intelligent Manufacturing with Generative Adversarial Framework

Wang

Gan

et al. 2019

2019 1st International Conference on Industrial Artificial Intelligence (IAI)

View full text Add to dashboard Cite

Motivated by the global economy greatly shaped by the manufacturing technology, more research on the intelligent manufacturing is studied. This paper firstly introduces an energy monitoring and data acquisition system namely the Point Energy Technology, which has been developed by the team and installed in a local bakery. While there is always lack of data because of various reasons, such as measurement or transmission mistakes during data collection. To solve this problem, we introduce a generative adversarial framework which is based on a game theory for data augmentation. This framework consists of two multi-layer perceptron networks-generator and discriminator. The upgrade framework with Q-net that extracts the latent variables from real data is proposed. To control the number of parameters, Q-net shares the structure with discriminator except the last layer. In addition, the two optimization methods, mini-batch gradient descent and adaptive moment estimation are adopted to tune the parameters. To evaluate the performance of these algorithms, the collected data from baking process is used in the experiment. Considering the reality, the missing data is processed into the state of missing completely at random (non-time series missing data). The experimental results illustrate that the latent generative adversarial framework with adaptive moment estimation could generated samples of good quality for non-time series missing data.

show abstract

Power Modeling for an Industrial Installation

Cited by 4 publications

References 1 publication

A remote MQTT-based data monitoring system for energy efficiency in industrial environments

A remote MQTT-based data monitoring system for energy efficiency in industrial environments

Analysis of energy saving potentials in intelligent manufacturing: A case study of bakery plants

Data Augmentation for Intelligent Manufacturing with Generative Adversarial Framework

Contact Info

Product

Resources

About