Methodology and model for predicting energy consumption in manufacturing at multiple scales

Reimann, Jan; Wenzel, Ken; Friedemann, Marko; Pütz, Matthias

doi:10.1016/j.promfg.2018.02.173

Cited by 5 publications

(3 citation statements)

References 9 publications

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“…Operator mistakes such as a lack of maintenance, aggressive running of machines, poor quality of materials, excessive speed, and high pressure can all have a significant impact on machines' power usage. However, most real-world industrial streaming data do not include all these changes in machine operations [38], [40]. In order to make a more accurate prediction of energy usage, the issue of uncertainty or unknown dynamics in streaming data must first be addressed [8], [41].…”

Section: Methodsmentioning

confidence: 99%

Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines

2022

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Industrial machinery is a significant energy consumer, and its CO 2 emissions have increased dramatically in recent years. Therefore, energy efficiency is becoming crucial for businesses, governments, as well as the planet. Estimating the power consumption of industrial machines with greater accuracy assists management and optimizes machine operation parameters. Real-time industrial machine datasets present several challenges, such as changes in the data over time, unknown running conditions, missing data, etc. Most research publications focus on the accuracy of traditional static models of forecasting; however, prediction performance deteriorates over time because data evolves. We implemented deep learning as a prediction model for three distinct real-world industrial datasets. The proposed method, dynamic modeling with memory (DMWM), improved overall prediction performance compared with conventional approaches by identifying concept drifts and optimizing the number of required models in response to industrial datasets' recurring machine energy consumption patterns.

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Section: Methodsmentioning

confidence: 99%

Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines

2022

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“…1. These layers represent multiple scales which improve the ability to automatically make decisions with different constraints, such as timing or granularity [29]. All together they compose the Intelligent Factory Space.…”

Section: General Architecture Of the Ifsmentioning

confidence: 99%

“…As a consequence, nothing is regarded in isolation and all data can be linked semantically (by their meaning). Linking data has the advantage that new relationships are established by which new knowledge can be derived automatically [29]. The layered and hierarchical organization spans a tree by means of a fog-based architecture and has the advantage that only relevant information is processed at every layer and required results can be communicated back fast when needed.…”

Section: Layer 3: Global Factory Environmentmentioning

confidence: 99%

The Intelligent Factory Space – A Concept for Observing, Learning and Communicating in the Digitalized Factory

Reimann

Sziebig

2019

IEEE Access

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The current and most important request for industrial automation is from Small and Mediumsized Enterprisess (SMEs). This is due to the fact that the SMEs need to increase their competitiveness to withstand the trend of outsourcing to low-cost countries. However, there are good examples of the strong competitiveness of the European SMEs, when they are able to utilize industrial automation for repetitive work, while utilizing the human labor for tasks requiring human skills, like sensing, flexibility, and cognitive skills. The Intelligent Factory Space (IFS) concept represents a framework for interaction between human and an automated system (digital factory). The IFS is composed of multiple layers (representing different services for the human user) and many modular components, which can be extended to the users' requirements. The IFS relies on industrial standards to communicate with existing machines while using novel two-way communication possibilities to feedback to the human user. In this paper, the general concept for the IFS is presented along with a reference implementation, where the concept is implemented in the situation of the human-robot collaboration.INDEX TERMS Cyber-physical systems, industrial cyber-physical systems, flexible manufacturing systems, intelligent shopfloor, connectivity, interoperability.

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Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine

Morris

Clancy

Hryshchenko

et al. 2022

Lecture Notes in Computer Science

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Methodology and model for predicting energy consumption in manufacturing at multiple scales

Cited by 5 publications

References 9 publications

Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines

Dynamic Modeling With Integrated Concept Drift Detection for Predicting Real-Time Energy Consumption of Industrial Machines

The Intelligent Factory Space – A Concept for Observing, Learning and Communicating in the Digitalized Factory

Using Model Selection and Reduction to Develop an Empirical Model to Predict Energy Consumption of a CNC Machine

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