An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops

Zhang, Chaoyang; Wang, Zhengxu; Ding, Kai; Chan, Felix T. S.; Ji, Weixi

doi:10.1080/00207543.2020.1748904

Cited by 31 publications

(15 citation statements)

References 47 publications

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“…These solutions must be adaptive to change and adjust to both assessment and actions front after implementing training from newly developed information. It means that their self-adjustment methods are continuously modified and changed during operation, preventing unwanted downtimes for the re-deployment of the systems and further programming work [29]. Furthermore, it is necessary to consider the universality of technologies to be readily moved and implemented to a wide range of production situations and fields.…”

Section: Background To the Cyber-physical Systemmentioning

confidence: 99%

RETRACTED ARTICLE: Optimized intelligent data management framework for a cyber-physical system for computational applications

Alsufyani

Alotaibi

Almagrabi

et al. 2021

Complex Intell. Syst.

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Data management is one obstacle in the production sector to be reconfigured and adapted through optimum parameterization in industry cyber-physical systems. This paper presents an intelligent data management framework for a cyber-physical system (IDMF-CPS) with machine-learning methods. A training approach based on two enhanced training procedures, running concurrently to upgrade the processing and communication strategy and the predictive models, is contained in the suggested reasoning modules. The method described spreads computational and analytical engines in several levels and autonomous modules to enhance intelligence and autonomy for controlling and tracking behavior on the work floor. The appropriateness of the suggested solution is supported by rapid reaction time and a suitable establishment of optimal operating variables for the required quality during macro- and micro-operations.

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Section: Background To the Cyber-physical Systemmentioning

confidence: 99%

RETRACTED ARTICLE: Optimized intelligent data management framework for a cyber-physical system for computational applications

Alsufyani

Alotaibi

Almagrabi

et al. 2021

Complex Intell. Syst.

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show abstract

“…Zhang et al [26] proposed an energy-aware cyber-physical system in which energyrelated BD and production-related BD play a vital role. The datasets were originated from energy monitors (sensors) mounted on machine tools and on gas, liquid, and cutting fluid circulation devices.…”

Section: Literature Reviewmentioning

confidence: 99%

Preparing Datasets of Surface Roughness for Constructing Big Data from the Context of Smart Manufacturing and Cognitive Computing

Fattahi

Okamoto

Ura

2021

BDCC

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In smart manufacturing, human-cyber-physical systems host digital twins and IoT-based networks. The networks weave manufacturing enablers such as CNC machine tools, robots, CAD/CAM systems, process planning systems, enterprise resource planning systems, and human resources. The twins work as the brains of the enablers; that is, the twins supply the required knowledge and help enablers solve problems autonomously in real-time. Since surface roughness is a major concern of all manufacturing processes, twins to solve surface roughness-relevant problems are needed. The twins must machine-learn the required knowledge from the relevant datasets available in big data. Therefore, preparing surface roughness-relevant datasets to be included in the human-cyber-physical system-friendly big data is a critical issue. However, preparing such datasets is a challenge due to the lack of a steadfast procedure. This study sheds some light on this issue. A state-of-the-art method is proposed to prepare the said datasets for surface roughness, wherein each dataset consists of four segments: semantic annotation, roughness model, simulation algorithm, and simulation system. These segments provide input information for digital twins’ input, modeling, simulation, and validation modules. The semantic annotation segment boils down to a concept map. A human- and machine-readable concept map is thus developed where the information of other segments (roughness model, simulation algorithm, and simulation system) is integrated. The delay map of surface roughness profile heights plays a pivotal role in the proposed dataset preparation method. The successful preparation of datasets of surface roughness underlying milling, turning, grinding, electric discharge machining, and polishing shows the efficacy of the proposed method. The method will be extended to the manufacturing processes in the next phase of this study.

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“…tool wear, machine tool degradation, etc. [30] Since dominant anomalies are easy to be detected, only the recessive anomalies are analyzed in this study, i.e., tool wear and machine degradation. These two anomalies are highly related with energy consumption [31].…”

Section: ) Energy Data Modeling For Production Anomaly Analysismentioning

confidence: 99%

Edge Computing Enabled Production Anomalies Detection and Energy-Efficient Production Decision Approach for Discrete Manufacturing Workshops

Zhang

2020

IEEE Access

Self Cite

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Due to the complexity and dynamics of manufacturing processes, there are various production anomalies in a discrete manufacturing workshop, which have a strong impact on manufacturing quality and productivity. Meanwhile, with the rapid development of Internet of Things technology and communication technology, data store and timely response become new challenges for production anomalies detection. Thus, an edge computing enabled production anomalies detection and energy-efficient production decision approach is proposed in this study. Firstly, an architecture of edge computing enabled production anomalies detection and energy-efficient rescheduling approach is introduced. Then, considering that the raw energy data are always large, isolated and messy, an energy consumption data preprocessing algorithm is established, and production anomaly analysis model is constructed based on long short-term memory network. When an anomaly occurs, an energy-efficient production decision making will be triggered. Finally, through a case analysis of a milling manufacturing system, the results show that the anomaly detection error of the proposed method is only 3.5%. This method realizes the combination of energy consumption data and manufacturing system anomalies detection, and can further assist production process monitoring and energy conservation. INDEX TERMS Edge computing; production anomalies detection; energy-efficient; long short-term memory network; discrete manufacturing workshops.

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An energy-aware cyber physical system for energy Big data analysis and recessive production anomalies detection in discrete manufacturing workshops

Cited by 31 publications

References 47 publications

RETRACTED ARTICLE: Optimized intelligent data management framework for a cyber-physical system for computational applications

RETRACTED ARTICLE: Optimized intelligent data management framework for a cyber-physical system for computational applications

Preparing Datasets of Surface Roughness for Constructing Big Data from the Context of Smart Manufacturing and Cognitive Computing

Edge Computing Enabled Production Anomalies Detection and Energy-Efficient Production Decision Approach for Discrete Manufacturing Workshops

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