An Internet of Things based energy efficiency monitoring and management system for machining workshop

Chen, Xingzheng; Li, Congbo; Tang, Ying; Xiao, Qinge

doi:10.1016/j.jclepro.2018.07.211

Cited by 69 publications

(30 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Precious water should be mentioned in mandatory criteria. Besides, some mandatory criteria about resourceful energy are considered due to the increasing pressure from energy price and environmental directive, which have forced machinery manufacturers to operate energy efficiency monitoring and management to improve economic benefits and environmental performance [27]. According to wide use of timber, chromated copper arsenate (CCA) treated timber elimination with the aim to minimize toxic effects of chromate copper arsenate treated timber on people and the environment is designed.…”

Section: Impact Of Mandatory Indicatorsmentioning

confidence: 99%

An Introduction to Neighborhood Sustainability Assessment Tool (NSAT) Study for China from Comprehensive Analysis of Eight Asian Tools

et al. 2020

View full text Add to dashboard Cite

In comparison to city-level and building-level sustainability research, neighborhood-level sustainable urban development is less studied. One of the ways of achieving sustainability at this level is the use of the Neighborhood Sustainability Assessment Tool (NSAT), which focuses on the sustainable urban development of districts, communities, and neighborhoods. NSAT is comprised of urban sustainable indicators and associated points ascribed towards achieving specific urban agendas, called headline sustainability indicators (HSIs) and themes. In China, neighborhood-level sustainability agenda has just been recently established in 2017. Hence, there is an immediate need for NSAT development of multiple cities responding to specific regions of different climate zones in China. As an example, this study utilizes the case of Ningbo City, located in east China, for such NSAT development. This paper provides a comprehensive analytical and comparison study of eight Asian NSATs to highlight compatibilities and extract specific indicators for a new NSAT development for China. The results from this comparative and analytical study, developed through a multidimensional approach of sustainable pathway model (SPM) inform a new NSAT development in a new context. This novel contribution is significant in a context where neighborhood sustainability measures are recently developed. This study serves as the starting point for future research of NSATs in China and other countries.

show abstract

Section: Impact Of Mandatory Indicatorsmentioning

confidence: 99%

An Introduction to Neighborhood Sustainability Assessment Tool (NSAT) Study for China from Comprehensive Analysis of Eight Asian Tools

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Schlechtendahl et al developed an XML-based Energy Information Description Language (EIDL) to store and manage the data of energy consumption in the machines [23]. Chen et al acquired the real-time spindle speed and feed rate through communication with the CNC system to monitor the energy efficiency during the machining processes [24]. In spite of these efforts, it is difficult to retrieve the information about energy consumption at each state during the machining processes because not all machines have PLCs, or other digital control systems, readily available to provide such control information, especially the legacy machines.…”

Section: Research Backgroundmentioning

confidence: 99%

“…switch-off (off), stop (stop), pre-processing (Pre-processing), ready (Ready) and executed a CAM Program on a workpiece (Processing) [31]. Chen et al focused on the machining center and used the following states for energy efficiency analysis, including power off state, start-up state, standby state, spindle acceleration/deceleration state, air cutting state and cutting state [24]. In the present study, we focus on the machining process of workpieces and hence are less concerned about switch on and off of the machine tool.…”

Section: Experimental Design and Materialsmentioning

confidence: 99%

Sensor-Based Recurrence Analysis of Energy Efficiency in Machining Processes

Wang

Yang

2020

IEEE Access

View full text Add to dashboard Cite

Manufacturing industry incurs a large portion of energy consumption and carbon emission in the economy. Traditionally, smart energy management depends on aggregated measures from billing information, as well as physics-based models, empirical results derived from extensive experiments, which tend to be limited in the ability for real-time monitoring of energy efficiency. There is an urgent need to develop energy monitoring solutions for more transparency about energy use. This paper presents a new sensor-based approach for recurrence analysis of continuous power signals and multi-state modeling of energy efficiency in the machining process. First, we leverage the recurrence plot to characterize the nonlinear variations in power signals and further help delineate different states in the machining process, thereby providing statistics of energy consumption in each state. Second, we compute the composite index of energy efficiency for each workpiece and then develop multivariate statistical control charts for process monitoring of continuous production of workpieces. Third, after an anomaly is detected, we propose the orthogonal decomposition approach to diagnose the root cause of abnormal states in the energy use. The proposed methodology is evaluated and validated on real-world manufacturing of shaft-like parts in a machine shop. Experimental results show that the prediction error of energy efficiency from sensorbased models is within 5% from the ground truth, which show great potentials to implement sensor-based monitoring and analysis of real-time energy efficiency in the manufacturing process. INDEX TERMS Energy efficiency, process monitoring, recurrence analysis, root cause diagnosis, manufacturing process, sustainable manufacturing.

show abstract

“…Second, the models that aid these opti mality tradeoffs will be driven by the Internet of Things, artificial intelli gence, and cognition. In other words, the models will continuously learn from realtime data that humans and machines in the factory generate, as well as similar machines in other facto ries and also simulations [8], [9]. Third, additive manufacturing will completely change not only the design of products but also the structure of supply chains through decentralized flexible produc tion, volumeindependent cost, and zerocost customization.…”

Section: Future Directionsmentioning

confidence: 99%

Automation Technologies for Sustainable Production [TC Spotlight]

Matta

2019

IEEE Robot. Automat. Mag.

View full text Add to dashboard Cite

An Internet of Things based energy efficiency monitoring and management system for machining workshop

Cited by 69 publications

References 27 publications

An Introduction to Neighborhood Sustainability Assessment Tool (NSAT) Study for China from Comprehensive Analysis of Eight Asian Tools

An Introduction to Neighborhood Sustainability Assessment Tool (NSAT) Study for China from Comprehensive Analysis of Eight Asian Tools

Sensor-Based Recurrence Analysis of Energy Efficiency in Machining Processes

Automation Technologies for Sustainable Production [TC Spotlight]

Contact Info

Product

Resources

About