Classification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems

Cao, Le; Pang, Chee Khiang; Gan, Oon Peen; Chee, Xiang Min; Zhang, Dan Hong; Luo, Ming; Chan, Hian Leng; Lewis, Frank L.

doi:10.1177/0142331212460883

Cited by 35 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Le et al 11 created an algorithm to process real-time energy data in order to distinguish various operational states, thereby decreasing the number of sensors required. Oliver et al categorized machining processes as either steady state or transient state and developed an energy monitoring approach for machine components.…”

Section: Categorical Literature Reviewmentioning

confidence: 99%

Energy efficiency of machining operations: A review

Moradnazhad

Ünver

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

Manufacturing processes are among the most energy intensive on earth. As negative ecological and economic impacts increase, reducing energy consumption is becoming critically important. In this article, a comprehensive overview of energy-saving strategies and opportunities for increasing energy efficiency in manufacturing operations is presented, with a focus on metal cutting processes. The issues and approaches involved in energy efficiency of machine tools and machining operations are reported in the literature and a structured research methodology is proposed for this purpose including prediction and modelling of machine energy consumption, determining the relationship between process energy consumption and process variables for material removal processes and optimization of cutting parameters in order to reduce energy consumption. Numerous techniques for increasing energy efficiency in manufacturing processes are identified and summarized, strengths and weaknesses of previous studies are discussed and potential avenues for future research are suggested.

show abstract

Section: Categorical Literature Reviewmentioning

confidence: 99%

Energy efficiency of machining operations: A review

Moradnazhad

Ünver

2016

Proceedings of the Institution of Mechanical Engineers, Part B:

View full text Add to dashboard Cite

show abstract

“…Moreover, pattern-recognition techniques have been applied in many areas by scholars, such as in optical fibre sensors (Lyons and Lewis, 2000), energy-consumption patterns (Le et al, 2013) and the use of a neural network (Xu et al, 2014). However, there is little work on hand postures, especially for comparing the effects of different methods and control strategies of pattern recognition when considering high recognition.…”

Section: Introductionmentioning

confidence: 99%

The design of a recognition system for multi-function electrical rubber hand

You

Zhou²,

Huang

2016

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

There is an issue with the timely application of artificial intelligence to humankind, particularly to the disabled. In this paper, we propose a surface electromyography signal recognition system for prosthetic application to persons with disabled hands, which achieves simplicity, reliability, accuracy and a short response time by increasing the performance of each part of the system and the coordination between the interconnected components. First, a surface electromyography signal acquisition system was designed on the basis of the cost and processing speed. Second, a method called the ‘extreme value’ was carried out on the original signal containing five continuous movements, by separating the signal into isolated segments representing different postures, which made application of the system for daily use possible. Third, on the basis of time-domain, chaos-theory and time–frequency-domain analysis methods, four features, namely the average amplitude, fractal dimension, maximum Lyapunov exponent and wavelet coefficient, were extracted from four possible arm locations to be determined. Furthermore, the average amplitude from the extensor digitorum and the wavelet coefficient from the flexor pollicis longus were determined as the final features after comparing the clustering effects of the extracted features. Finally, a new strategy for classifying the different postures based on a back-propagation neural network was introduced to obtain an average system accuracy of 82.77% for five continuous movements.

show abstract

“…Due to the possibility to collect a huge amount of production-relevant data [8], it is possible to use methods of data mining to obtain more insight into assembly processes and subsequently to identify top consumers and focus stations for example for energy saving projects.…”

Section: Introductionmentioning

confidence: 99%

Potentials for Energy Saving and Quality Improvement of Assembly Presses Using Data Mining

Sand

Seidl

Leinauer

et al. 2016

AMM

View full text Add to dashboard Cite

Modern assembly lines are usually optimized towards output and tact time as well as process capability and quality. Yet, approaches for energy saving are hardly used in assembly presses. Therefore, assembly lines are using more electric power and compressed air than necessary. Especially at high load, during handling phases and in different idle modes there is a huge potential for energy savings. Current research is focusing on high-power consuming turning and milling machines as well as laser welding. Energy saving projects usually focus on whole factory halls instead of manufacturing lines and single assembly machines. Therefore, this paper presents a new methodology using a top-down-approach and data mining analysis regarding a conventional assembly press as well as a whole assembly line. Here, relevant information types like process data, quality factors, expenditure of energy per produced part and power consumption are used to generate more insight into chained assembly processes. Various tools like energy analysis, process flow and correlation analysis are used to identify focus stations of a whole assembly line for energy saving projects and quality improvements. This novel holistic approach regards the electrical power and compressed air consumption of each relevant station and its machine components during different operating states as well as its correlations between process data, quality factors and energy consumption. Besides tact-time-analysis of the process, the scheduled and unplanned downtimes of the machine are also regarded. Furthermore, it enables predictions of tool wear and breakdown, quality impacts of supplied parts, as well as energy savings on process and machine level. Due to an increased quality, the material efficiency may rise as well.

show abstract

Classification of energy consumption patterns for energy audit and machine scheduling in industrial manufacturing systems

Cited by 35 publications

References 24 publications

Energy efficiency of machining operations: A review

Energy efficiency of machining operations: A review

The design of a recognition system for multi-function electrical rubber hand

Potentials for Energy Saving and Quality Improvement of Assembly Presses Using Data Mining

Contact Info

Product

Resources

About