A Deep Swarm-Optimized Model for Leveraging Industrial Data Analytics in Cognitive Manufacturing

Kumar, Akshi; Jaiswal, Arunima

doi:10.1109/tii.2020.3005532

Cited by 39 publications

(29 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As previously mentioned, the EDM optimizes through evolutionary computation such as genetic algorithms [24]- [26] and swarm intelligence [27]- [29]. In this study, the artificial bee colony (ABC) algorithm [30]- [32] is applied as the optimization method.…”

Section: B Optimization Methodsmentioning

confidence: 99%

Variation Model Abstraction and Adaptive Control Based on Element Description Method Toward Smart Factory

Takeuchi¹,

Katsura

2021

IEEE Open J. Ind. Electron. Soc.

View full text Add to dashboard Cite

In this study, a method for realizing an intelligent production process by reducing quality variation in the manufacturing industry is proposed. A quality fluctuation model in a production process is abstracted, and the quality is improved using adaptation rules based on the model. In this framework, the value directly related to product quality is expressed as multiplying the coefficient and the setting parameter. This expression makes it possible to regard the quality variations as being caused by the coefficient variations. Hence, it is possible to reduce the variation of quality by predicting the fluctuation of the coefficient from various data acquired from the production line and increasing or decreasing the setting parameter based on the predicted value. Moreover, the element description method is applied to predict the fluctuation of the coefficient. The element description method has the advantages of a model-based method whose physical meaning can be understood and the advantages of a database method applicable to an unknown system. Therefore, the mechanism of fluctuation can be abstracted and can be used as explicit knowledge. In this study, this framework is applied to reduce the variation in filling weight of the powder filling process and is demonstrated. As a result, the filling weight variation has been reduced by approximately 33 %.

show abstract

Section: B Optimization Methodsmentioning

confidence: 99%

Variation Model Abstraction and Adaptive Control Based on Element Description Method Toward Smart Factory

Takeuchi¹,

Katsura

2021

IEEE Open J. Ind. Electron. Soc.

View full text Add to dashboard Cite

show abstract

“…Tree-based models are widely used to predict critical bus voltage and load in PV buses [ 27 ], the heat load of residential buildings [ 28 ], and the hourly performance forecasting of ground source heat pump systems [ 29 ]. The use of tree-based methods in industry have also been investigated by scholars such as the directed edge weight prediction of industrial Internet of Things [ 30 ] and the analysis of industrial data in cognitive manufacturing [ 31 ]. Regression trees split the training dataset into distinct and non-overlapping regions, which is inappropriate for energy consumption forecasting in cement manufacturing because of the strong coupling in cement production data.…”

Section: Related Workmentioning

confidence: 99%

A Synchronous Prediction Model Based on Multi-Channel CNN with Moving Window for Coal and Electricity Consumption in Cement Calcination Process

Shi

Huang

Hao

et al. 2021

Sensors

View full text Add to dashboard Cite

The precision and reliability of the synchronous prediction of multi energy consumption indicators such as electricity and coal consumption are important for the production optimization of industrial processes (e.g., in the cement industry) due to the deficiency of the coupling relationship of the two indicators while forecasting separately. However, the time lags, coupling, and uncertainties of production variables lead to the difficulty of multi-indicator synchronous prediction. In this paper, a data driven forecast approach combining moving window and multi-channel convolutional neural networks (MWMC-CNN) was proposed to predict electricity and coal consumption synchronously, in which the moving window was designed to extract the time-varying delay feature of the time series data to overcome its impact on energy consumption prediction, and the multi-channel structure was designed to reduce the impact of the redundant parameters between weakly correlated variables of energy prediction. The experimental results implemented by the actual raw data of the cement plant demonstrate that the proposed MWMC-CNN structure has a better performance than without the combination structure of the moving window multi-channel with convolutional neural network.

show abstract

“…However, when compared to SVMs, decision trees' learning speed is very high. Because they score high on the key features of data mining, decision trees have been widely used in exploration and prediction problems [51].…”

Section: Machine Learning Algorithmsmentioning

confidence: 99%

“…this is a multilayer feedforward artificial neural network, which was first proposed for two-dimensional image pro- cessing. In CNN's, the featured learning is accomplished by alternating and stacking convolutional layers and merging operations [51]. The convolutional layer uses multiple local kernel filters to convolve with the original input data and generate invariant local features.…”

Section: ) Convolutional Neural Network (Cnn)mentioning

confidence: 99%

Cooperative Robotics and Machine Learning for Smart Manufacturing: Platform Design and Trends Within the Context of Industrial Internet of Things

Lins

Givigi

2021

IEEE Access

View full text Add to dashboard Cite

Internet of Things (IoT) in industrial settings now leads to the development of a new generation of systems designed to improve the operational efficiency of the new paradigm of smart manufacturing plants. Thereby, the current article introduces in detail the definitions, concepts, standards, and other important aspects related to smart manufacturing, cooperative robotics, and Machine Learning techniques (ML). The paper highlights the opportunities presented by the new paradigm and the challenges faced in effectively implementing it in the industrial context. Especially, the focus is on the challenges associated with the architectures, communications technology, and protocols that enable the integration and deployment of machine learning algorithms to improve the execution of tasks daily performed by the collaboration of human operators, machines, and robots. Finally, it also provides a systematic review of state-of-the-art research efforts for the fields aforementioned, and a designed platform for integrating collaborative robotics and machine learning established on six layers and four hierarchies of the RAMI 4.0 (Reference Architectural Model Industry 4.0) is presented.

show abstract

A Deep Swarm-Optimized Model for Leveraging Industrial Data Analytics in Cognitive Manufacturing

Cited by 39 publications

References 27 publications

Variation Model Abstraction and Adaptive Control Based on Element Description Method Toward Smart Factory

Variation Model Abstraction and Adaptive Control Based on Element Description Method Toward Smart Factory

A Synchronous Prediction Model Based on Multi-Channel CNN with Moving Window for Coal and Electricity Consumption in Cement Calcination Process

Cooperative Robotics and Machine Learning for Smart Manufacturing: Platform Design and Trends Within the Context of Industrial Internet of Things

Contact Info

Product

Resources

About